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Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review

Ahmadi H, Gholamzadeh M, Shahmoradi L, Nilashi M, Rashvand P

Comput Methods Programs Biomed, 2018 Jul;161:145-172.
PMID: 29852957 DOI: 10.1016/j.cmpb.2018.04.013

BACKGROUND AND OBJECTIVE: Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices.
METHODS: Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.
RESULTS: Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.
CONCLUSIONS: Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Digital image processing and analysis for activated sludge wastewater treatment

Khan MB, Lee XY, Nisar H, Ng CA, Yeap KH, Malik AS

Adv Exp Med Biol, 2015;823:227-48.
PMID: 25381111 DOI: 10.1007/978-3-319-10984-8_13

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Diagnostic ability of computed tomography using DentaScan software in endodontics: case reports

Siotia J, Gupta SK, Acharya SR, Saraswathi V

Int J Comput Dent, 2011;14(4):321-34.
PMID: 22324223

Radiographic examination is essential in diagnosis and treatment planning in endodontics. Conventional radiographs depict structures in two dimensions only. The ability to assess the area of interest in three dimensions is advantageous. Computed tomography is an imaging technique which produces three-dimensional images of an object by taking a series of two-dimensional sectional X-ray images. DentaScan is a computed tomography software program that allows the mandible and maxilla to be imaged in three planes: axial, panoramic, and cross-sectional. As computed tomography is used in endodontics, DentaScan can play a wider role in endodontic diagnosis. It provides valuable information in the assessment of the morphology of the root canal, diagnosis of root fractures, internal and external resorptions, pre-operative assessment of anatomic structures etc. The aim of this article is to explore the clinical usefulness of computed tomography and DentaScan in endodontic diagnosis, through a series of four cases of different endodontic problems.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Diabetic retinopathy: a quadtree based blood vessel detection algorithm using RGB components in fundus images

Reza AW, Eswaran C, Hati S

J Med Syst, 2008 Apr;32(2):147-55.
PMID: 18461818

Blood vessel detection in retinal images is a fundamental step for feature extraction and interpretation of image content. This paper proposes a novel computational paradigm for detection of blood vessels in fundus images based on RGB components and quadtree decomposition. The proposed algorithm employs median filtering, quadtree decomposition, post filtration of detected edges, and morphological reconstruction on retinal images. The application of preprocessing algorithm helps in enhancing the image to make it better fit for the subsequent analysis and it is a vital phase before decomposing the image. Quadtree decomposition provides information on the different types of blocks and intensities of the pixels within the blocks. The post filtration and morphological reconstruction assist in filling the edges of the blood vessels and removing the false alarms and unwanted objects from the background, while restoring the original shape of the connected vessels. The proposed method which makes use of the three color components (RGB) is tested on various images of publicly available database. The results are compared with those obtained by other known methods as well as with the results obtained by using the proposed method with the green color component only. It is shown that the proposed method can yield true positive fraction values as high as 0.77, which are comparable to or somewhat higher than the results obtained by other known methods. It is also shown that the effect of noise can be reduced if the proposed method is implemented using only the green color component.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Development of a kidney TeleUltrasound consultation system

Hassan A, Ibrahim F

J Digit Imaging, 2011 Apr;24(2):308-13.
PMID: 20386951 DOI: 10.1007/s10278-010-9283-8

This paper presents the development of kidney TeleUltrasound consultation system. The TeleUltrasound system provides an innovative design that aids the acquisition, archiving, and dissemination of medical data and information over the internet as its backbone. The system provides data sharing to allow remote collaboration, viewing, consultation, and diagnosis of medical data. The design is layered upon a standard known as Digital Imaging and Communication in Medicine (DICOM). The DICOM standard defines protocols for exchanging medical images and their associated data. The TeleUltrasound system is an integrated solution for retrieving, processing, and archiving images and providing data storage management using Structured Query Language (SQL) database. Creating a web-based interface is an additional advantage to achieve global accessibility of experts that will widely open the opportunity of greater examination and multiple consultations. This system is equipped with a high level of data security and its performance has been tested with white, black, and gray box techniques. And the result was satisfactory. The overall system has been evaluated by several radiologists in Malaysia, United Arab Emirates, and Sudan, the result is shown within this paper.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Determination of foveal avascular zone in diabetic retinopathy digital fundus images

Ahmad Fadzil MH, Izhar LI, Nugroho HA

Comput Biol Med, 2010 Jul;40(7):657-64.
PMID: 20573343 DOI: 10.1016/j.compbiomed.2010.05.004

Monitoring FAZ area enlargement enables physicians to monitor progression of the DR. At present, it is difficult to discern the FAZ area and to measure its enlargement in an objective manner using digital fundus images. A semi-automated approach for determination of FAZ using color images has been developed. Here, a binary map of retinal blood vessels is computer generated from the digital fundus image to determine vessel ends and pathologies surrounding FAZ for area analysis. The proposed method is found to achieve accuracies from 66.67% to 98.69% compared to accuracies of 18.13-95.07% obtained by manual segmentation of FAZ regions from digital fundus images.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Design and development of a content-based medical image retrieval system for spine vertebrae irregularity

Mustapha A, Hussain A, Samad SA, Zulkifley MA, Diyana Wan Zaki WM, Hamid HA

Biomed Eng Online, 2015;14:6.
PMID: 25595511 DOI: 10.1186/1475-925X-14-6

Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space

Fallahpoor M, Chakraborty S, Pradhan B, Faust O, Barua PD, Chegeni H, et al.

Comput Methods Programs Biomed, 2024 Jan;243:107880.
PMID: 37924769 DOI: 10.1016/j.cmpb.2023.107880

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things

Iqbal U, Wah TY, Habib Ur Rehman M, Mujtaba G, Imran M, Shoaib M

J Med Syst, 2018 Nov 05;42(12):252.
PMID: 30397730 DOI: 10.1007/s10916-018-1107-2

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology-Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method

Gupta R, Elamvazuthi I, Dass SC, Faye I, Vasant P, George J, et al.

Biomed Eng Online, 2014;13:157.
PMID: 25471386 DOI: 10.1186/1475-925X-13-157

Disorders of rotator cuff tendons results in acute pain limiting the normal range of motion for shoulder. Of all the tendons in rotator cuff, supraspinatus (SSP) tendon is affected first of any pathological changes. Diagnosis of SSP tendon using ultrasound is considered to be operator dependent with its accuracy being related to operator's level of experience.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Correlation between subjective and objective assessment of magnetic resonance (MR) images

Chow LS, Rajagopal H, Paramesran R, Alzheimer's Disease Neuroimaging Initiative

Magn Reson Imaging, 2016 07;34(6):820-831.
PMID: 26969762 DOI: 10.1016/j.mri.2016.03.006

Medical Image Quality Assessment (IQA) plays an important role in assisting and evaluating the development of any new hardware, imaging sequences, pre-processing or post-processing algorithms. We have performed a quantitative analysis of the correlation between subjective and objective Full Reference - IQA (FR-IQA) on Magnetic Resonance (MR) images of the human brain, spine, knee and abdomen. We have created a MR image database that consists of 25 original reference images and 750 distorted images. The reference images were distorted with six types of distortions: Rician Noise, Gaussian White Noise, Gaussian Blur, DCT compression, JPEG compression and JPEG2000 compression, at various levels of distortion. Twenty eight subjects were chosen to evaluate the images resulting in a total of 21,700 human evaluations. The raw scores were then converted to Difference Mean Opinion Score (DMOS). Thirteen objective FR-IQA metrics were used to determine the validity of the subjective DMOS. The results indicate a high correlation between the subjective and objective assessment of the MR images. The Noise Quality Measurement (NQM) has the highest correlation with DMOS, where the mean Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are 0.936 and 0.938 respectively. The Universal Quality Index (UQI) has the lowest correlation with DMOS, where the mean PLCC and SROCC are 0.807 and 0.815 respectively. Student's T-test was used to find the difference in performance of FR-IQA across different types of distortion. The superior IQAs tested statistically are UQI for Rician noise images, Visual Information Fidelity (VIF) for Gaussian blur images, NQM for both DCT and JPEG compressed images, Peak Signal-to-Noise Ratio (PSNR) for JPEG2000 compressed images.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Computerized segmentation and measurement of chronic wound images

Ahmad Fauzi MF, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN

Comput Biol Med, 2015 May;60:74-85.
PMID: 25756704 DOI: 10.1016/j.compbiomed.2015.02.015

An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Computerised image analysis of vitiligo lesion: evaluation using manually defined lesion areas

Nugroho H, Ahmad Fadzil MH, Shamsudin N, Hussein SH

Skin Res Technol, 2013 Feb;19(1):e72-7.
PMID: 22233154 DOI: 10.1111/j.1600-0846.2011.00610.x

Vitiligo is a cutaneous pigmentary disorder characterized by depigmented macules and patches that result from loss of epidermal melanocytes. Physician evaluates the efficacy of treatment by comparing the extent of vitiligo lesions before and after treatment based on the overall visual impression of the treatment response. This method is called the physician's global assessment (PGA) which is subjective. In this article, we present an innovative digital image processing method to determine vitiligo lesion area in an objective manner.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Computer-assisted quantification of CD3+ T cells in follicular lymphoma

Abas FS, Shana'ah A, Christian B, Hasserjian R, Louissaint A, Pennell M, et al.

Cytometry A, 2017 06;91(6):609-621.
PMID: 28110507 DOI: 10.1002/cyto.a.23049

The advance of high resolution digital scans of pathology slides allowed development of computer based image analysis algorithms that may help pathologists in IHC stains quantification. While very promising, these methods require further refinement before they are implemented in routine clinical setting. Particularly critical is to evaluate algorithm performance in a setting similar to current clinical practice. In this article, we present a pilot study that evaluates the use of a computerized cell quantification method in the clinical estimation of CD3 positive (CD3+) T cells in follicular lymphoma (FL). Our goal is to demonstrate the degree to which computerized quantification is comparable to the practice of estimation by a panel of expert pathologists. The computerized quantification method uses entropy based histogram thresholding to separate brown (CD3+) and blue (CD3-) regions after a color space transformation. A panel of four board-certified hematopathologists evaluated a database of 20 FL images using two different reading methods: visual estimation and manual marking of each CD3+ cell in the images. These image data and the readings provided a reference standard and the range of variability among readers. Sensitivity and specificity measures of the computer's segmentation of CD3+ and CD- T cell are recorded. For all four pathologists, mean sensitivity and specificity measures are 90.97 and 88.38%, respectively. The computerized quantification method agrees more with the manual cell marking as compared to the visual estimations. Statistical comparison between the computerized quantification method and the pathologist readings demonstrated good agreement with correlation coefficient values of 0.81 and 0.96 in terms of Lin's concordance correlation and Spearman's correlation coefficient, respectively. These values are higher than most of those calculated among the pathologists. In the future, the computerized quantification method may be used to investigate the relationship between the overall architectural pattern (i.e., interfollicular vs. follicular) and outcome measures (e.g., overall survival, and time to treatment). © 2017 International Society for Advancement of Cytometry.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Fulltext Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG)

Al-Faris AQ, Ngah UK, Isa NA, Shuaib IL

J Digit Imaging, 2014 Feb;27(1):133-44.
PMID: 24100762 DOI: 10.1007/s10278-013-9640-5

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images

Abdulhay E, Mohammed MA, Ibrahim DA, Arunkumar N, Venkatraman V

J Med Syst, 2018 Feb 17;42(4):58.
PMID: 29455440 DOI: 10.1007/s10916-018-0912-y

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches

Abu A, Leow LK, Ramli R, Omar H

BMC Bioinformatics, 2016 Dec 22;17(Suppl 19):505.
PMID: 28155645 DOI: 10.1186/s12859-016-1362-5

BACKGROUND: Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs.
RESULTS: At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community.
CONCLUSIONS: This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Ceph-X: development and evaluation of 2D cephalometric system

Mosleh MA, Baba MS, Malek S, Almaktari RA

BMC Bioinformatics, 2016 Dec 22;17(Suppl 19):499.
PMID: 28155649 DOI: 10.1186/s12859-016-1370-5

BACKGROUND: Cephalometric analysis and measurements of skull parameters using X-Ray images plays an important role in predicating and monitoring orthodontic treatment. Manual analysis and measurements of cephalometric is considered tedious, time consuming, and subjected to human errors. Several cephalometric systems have been developed to automate the cephalometric procedure; however, no clear insights have been reported about reliability, performance, and usability of those systems. This study utilizes some techniques to evaluate reliability, performance, and usability metric using SUS methods of the developed cephalometric system which has not been reported in previous studies.
METHODS: In this study a novel system named Ceph-X is developed to computerize the manual tasks of orthodontics during cephalometric measurements. Ceph-X is developed by using image processing techniques with three main models: enhancements X-ray image model, locating landmark model, and computation model. Ceph-X was then evaluated by using X-ray images of 30 subjects (male and female) obtained from University of Malaya hospital. Three orthodontics specialists were involved in the evaluation of accuracy to avoid intra examiner error, and performance for Ceph-X, and 20 orthodontics specialists were involved in the evaluation of the usability, and user satisfaction for Ceph-X by using the SUS approach.
RESULTS: Statistical analysis for the comparison between the manual and automatic cephalometric approaches showed that Ceph-X achieved a great accuracy approximately 96.6%, with an acceptable errors variation approximately less than 0.5 mm, and 1°. Results showed that Ceph-X increased the specialist performance, and minimized the processing time to obtain cephalometric measurements of human skull. Furthermore, SUS analysis approach showed that Ceph-X has an excellent usability user's feedback.
CONCLUSIONS: The Ceph-X has proved its reliability, performance, and usability to be used by orthodontists for the analysis, diagnosis, and treatment of cephalometric.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network

Abisha S, Mutawa AM, Murugappan M, Krishnan S

PLoS One, 2023;18(4):e0284021.
PMID: 37018344 DOI: 10.1371/journal.pone.0284021

Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India's agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf's-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%-without fusion, 87%-with fusion) in classifying leaf diseases.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Blood vessel segmentation in color fundus images based on regional and Hessian features

Shah SAA, Tang TB, Faye I, Laude A

Graefes Arch Clin Exp Ophthalmol, 2017 Aug;255(8):1525-1533.
PMID: 28474130 DOI: 10.1007/s00417-017-3677-y

PURPOSE: To propose a new algorithm of blood vessel segmentation based on regional and Hessian features for image analysis in retinal abnormality diagnosis.
METHODS: Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel.
RESULTS: The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy.
CONCLUSIONS: Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*

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