Displaying publications 21 - 40 of 113 in total

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  1. Lung nodule classification using deep Local-Global networks
    Al-Shabi M, Lan BL, Chan WY, Ng KH, Tan M
    Int J Comput Assist Radiol Surg, 2019 Oct;14(10):1815-1819.
    PMID: 31020576 DOI: 10.1007/s11548-019-01981-7
    PURPOSE: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.

    METHODS: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps.

    RESULTS: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by

    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  2. Fulltext Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch
    Fadilah N, Mohamad-Saleh J, Abdul Halim Z, Ibrahim H, Syed Ali SS
    Sensors (Basel), 2012;12(10):14179-95.
    PMID: 23202043 DOI: 10.3390/s121014179
    Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  3. Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework
    Than JCM, Saba L, Noor NM, Rijal OM, Kassim RM, Yunus A, et al.
    Comput Biol Med, 2017 10 01;89:197-211.
    PMID: 28825994 DOI: 10.1016/j.compbiomed.2017.08.014
    Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  4. Fulltext A chaotic cryptosystem for images based on Henon and Arnold cat map
    Soleymani A, Nordin MJ, Sundararajan E
    ScientificWorldJournal, 2014;2014:536930.
    PMID: 25258724 DOI: 10.1155/2014/536930
    The rapid evolution of imaging and communication technologies has transformed images into a widespread data type. Different types of data, such as personal medical information, official correspondence, or governmental and military documents, are saved and transmitted in the form of images over public networks. Hence, a fast and secure cryptosystem is needed for high-resolution images. In this paper, a novel encryption scheme is presented for securing images based on Arnold cat and Henon chaotic maps. The scheme uses Arnold cat map for bit- and pixel-level permutations on plain and secret images, while Henon map creates secret images and specific parameters for the permutations. Both the encryption and decryption processes are explained, formulated, and graphically presented. The results of security analysis of five different images demonstrate the strength of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time for both encryption and decryption processes guarantee that the cryptosystem can work effectively in real-time applications.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  5. Medical image compression using 3-D Hartley transform
    Shyam Sunder R, Eswaran C, Sriraam N
    Comput Biol Med, 2006 Sep;36(9):958-73.
    PMID: 16026779
    In this paper, 3-D discrete Hartley transform is applied for the compression of two medical modalities, namely, magnetic resonance images and X-ray angiograms and the performance results are compared with those of 3-D discrete cosine and Fourier transforms using the parameters such as PSNR and bit rate. It is shown that the 3-D discrete Hartley transform is better than the other two transforms for magnetic resonance brain images whereas for the X-ray angiograms, the 3-D discrete cosine transform is found to be superior.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  6. Assessment of 3-D nasal airway morphology in Southeast Asian adults with obstructive sleep apnea using acoustic rhinometry
    Banabilh SM, Suzina AH, Mohamad H, Dinsuhaimi S, Samsudin AR, Singh GD
    Clin Oral Investig, 2010 Oct;14(5):491-8.
    PMID: 19806371 DOI: 10.1007/s00784-009-0342-9
    The aim of the present study is to investigate nasal airway morphology in Asian adults with and without obstructive sleep apnea (OSA) using acoustic rhinometry (AR), principal components analysis (PCA), and 3-D finite-element analysis (FEA). One hundred eight adult Malays aged 18-65 years (mean ± SD, 33.2 ± 13.31) underwent clinical examination and limited channel polysomnography, providing 54 patients with OSA and 54 non-OSA controls. The mean minimal cross section area 1 (MCA1) and the mean minimal cross sectional area 2 (MCA2) were obtained from AR for all subjects and subjected to t tests. The OSA and control nasal airways were reconstructed in 3-D and subjected to PCA and FEA. The mean MCA1 and MCA2 using AR were found to be significantly smaller in the OSA group than in the control group (p < 0.001). Comparing the 3-D OSA and control nasal airways using PCA, the first two eigenvalues accounted for 94% of the total shape change, and statistical differences were found (p < 0.05). Similarly, comparing the nasal airways using FEA, the 3-D mean OSA nasal airway was significantly narrower in the OSA group compared to the control group. Specifically, decreases in size of approx. 10-22% were found in the nasal valve/head of inferior turbinate area. In conclusion, differences in nasal airway morphology are present when comparing patients with OSA to controls. These differences need to be recognized as they can improve our understanding of the etiological basis of obstructive sleep apnea and facilitate its subsequent management.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  7. Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images
    Hamoud Al-Tamimi MS, Sulong G, Shuaib IL
    Magn Reson Imaging, 2015 Jul;33(6):787-803.
    PMID: 25865822 DOI: 10.1016/j.mri.2015.03.008
    Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  8. 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*
  9. Microalgae identification: Future of image processing and digital algorithm
    Chong JWR, Khoo KS, Chew KW, Vo DN, Balakrishnan D, Banat F, et al.
    Bioresour Technol, 2023 Feb;369:128418.
    PMID: 36470491 DOI: 10.1016/j.biortech.2022.128418
    The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  10. Trends in digital image processing of isolated microalgae by incorporating classification algorithm
    Chong JWR, Khoo KS, Chew KW, Ting HY, Show PL
    Biotechnol Adv, 2023;63:108095.
    PMID: 36608745 DOI: 10.1016/j.biotechadv.2023.108095
    Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  11. 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*
  12. Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
    Ghanizadeh A, Abarghouei AA, Sinaie S, Saad P, Shamsuddin SM
    Appl Opt, 2011 Jul 1;50(19):3191-200.
    PMID: 21743518 DOI: 10.1364/AO.50.003191
    Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  13. Fulltext A Method for Quantifying, Visualising, and Analysing Gastropod Shell Form
    Liew TS, Schilthuizen M
    PLoS One, 2016;11(6):e0157069.
    PMID: 27280463 DOI: 10.1371/journal.pone.0157069
    Quantitative analysis of organismal form is an important component for almost every branch of biology. Although generally considered an easily-measurable structure, the quantification of gastropod shell form is still a challenge because many shells lack homologous structures and have a spiral form that is difficult to capture with linear measurements. In view of this, we adopt the idea of theoretical modelling of shell form, in which the shell form is the product of aperture ontogeny profiles in terms of aperture growth trajectory that is quantified as curvature and torsion, and of aperture form that is represented by size and shape. We develop a workflow for the analysis of shell forms based on the aperture ontogeny profile, starting from the procedure of data preparation (retopologising the shell model), via data acquisition (calculation of aperture growth trajectory, aperture form and ontogeny axis), and data presentation (qualitative comparison between shell forms) and ending with data analysis (quantitative comparison between shell forms). We evaluate our methods on representative shells of the genera Opisthostoma and Plectostoma, which exhibit great variability in shell form. The outcome suggests that our method is a robust, reproducible, and versatile approach for the analysis of shell form. Finally, we propose several potential applications of our methods in functional morphology, theoretical modelling, taxonomy, and evolutionary biology.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  14. 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
  15. Fulltext A preliminary study on automated freshwater algae recognition and classification system
    Mosleh MA, Manssor H, Malek S, Milow P, Salleh A
    BMC Bioinformatics, 2012;13 Suppl 17:S25.
    PMID: 23282059 DOI: 10.1186/1471-2105-13-S17-S25
    Freshwater algae can be used as indicators to monitor freshwater ecosystem condition. Algae react quickly and predictably to a broad range of pollutants. Thus they provide early signals of worsening environment. This study was carried out to develop a computer-based image processing technique to automatically detect, recognize, and identify algae genera from the divisions Bacillariophyta, Chlorophyta and Cyanobacteria in Putrajaya Lake. Literature shows that most automated analyses and identification of algae images were limited to only one type of algae. Automated identification system for tropical freshwater algae is even non-existent and this study is partly to fill this gap.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  16. Automated nuclei segmentation of malignant using level sets
    Husham A, Hazim Alkawaz M, Saba T, Rehman A, Saleh Alghamdi J
    Microsc Res Tech, 2016 Oct;79(10):993-997.
    PMID: 27476682 DOI: 10.1002/jemt.22733
    Segmentation of objects from a noisy and complex image is still a challenging task that needs to be addressed. This article proposed a new method to detect and segment nuclei to determine whether they are malignant or not (determination of the region of interest, noise removal, enhance the image, candidate detection is employed on the centroid transform to evaluate the centroid of each object, the level set [LS] is applied to segment the nuclei). The proposed method consists of three main stages: preprocessing, seed detection, and segmentation. Preprocessing stage involves the preparation of the image conditions to ensure that they meet the segmentation requirements. Seed detection detects the seed point to be used in the segmentation stage, which refers to the process of segmenting the nuclei using the LS method. In this research work, 58 H&E breast cancer images from the UCSB Bio-Segmentation Benchmark dataset are evaluated. The proposed method reveals the high performance and accuracy in comparison to the techniques reported in literature. The experimental results are also harmonized with the ground truth images.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  17. Fulltext Surface Reconstruction from Parallel Curves with Application to Parietal Bone Fracture Reconstruction
    Majeed A, Mt Piah AR, Ridzuan Yahya Z
    PLoS One, 2016;11(3):e0149921.
    PMID: 26967643 DOI: 10.1371/journal.pone.0149921
    Maxillofacial trauma are common, secondary to road traffic accident, sports injury, falls and require sophisticated radiological imaging to precisely diagnose. A direct surgical reconstruction is complex and require clinical expertise. Bio-modelling helps in reconstructing surface model from 2D contours. In this manuscript we have constructed the 3D surface using 2D Computerized Tomography (CT) scan contours. The fracture part of the cranial vault are reconstructed using GC1 rational cubic Ball curve with three free parameters, later the 2D contours are flipped into 3D with equidistant z component. The constructed surface is represented by contours blending interpolant. At the end of this manuscript a case report of parietal bone fracture is also illustrated by employing this method with a Graphical User Interface (GUI) illustration.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  18. Physiological assessment of in vivo human knee articular cartilage using sodium MR imaging at 1.5 T
    Hani AF, Kumar D, Malik AS, Razak R
    Magn Reson Imaging, 2013 Sep;31(7):1059-67.
    PMID: 23731535 DOI: 10.1016/j.mri.2013.01.007
    Osteoarthritis is a common joint disorder that is most prevalent in the knee joint. Knee osteoarthritis (OA) can be characterized by the gradual loss of articular cartilage (AC). Formation of lesion, fissures and cracks on the cartilage surface has been associated with degenerative AC and can be measured by morphological assessment. In addition, loss of proteoglycan from extracellular matrix of the AC can be measured at early stage of cartilage degradation by physiological assessment. In this case, a biochemical phenomenon of cartilage is used to assess the changes at early degeneration of AC. In this paper, a method to measure local sodium concentration in AC due to proteoglycan has been investigated. A clinical 1.5-T magnetic resonance imaging (MRI) with multinuclear spectroscopic facility is used to acquire sodium images and quantify local sodium content of AC. An optimised 3D gradient-echo sequence with low echo time has been used for MR scan. The estimated sodium concentration in AC region from four different data sets is found to be ~225±19mmol/l, which matches the values that has been reported for the normal AC. This study shows that sodium images acquired at clinical 1.5-T MRI system can generate an adequate quantitative data that enable the estimation of sodium concentration in AC. We conclude that this method is potentially suitable for non-invasive physiological (sodium content) measurement of articular cartilage.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  19. 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*
  20. Fulltext Image reconstruction of a charge coupled device based optical tomographic instrumentation system for particle sizing
    Idroas M, Rahim RA, Green RG, Ibrahim MN, Rahiman MH
    Sensors (Basel), 2010;10(10):9512-28.
    PMID: 22163423 DOI: 10.3390/s101009512
    This research investigates the use of charge coupled device (abbreviated as CCD) linear image sensors in an optical tomographic instrumentation system used for sizing particles. The measurement system, consisting of four CCD linear image sensors are configured around an octagonal shaped flow pipe for a four projections system is explained. The four linear image sensors provide 2,048 pixel imaging with a pixel size of 14 micron × 14 micron, hence constituting a high-resolution system. Image reconstruction for a four-projection optical tomography system is also discussed, where a simple optical model is used to relate attenuation due to variations in optical density, [R], within the measurement section. Expressed in matrix form this represents the forward problem in tomography [S] [R] = [M]. In practice, measurements [M] are used to estimate the optical density distribution by solving the inverse problem [R] = [S](-1)[M]. Direct inversion of the sensitivity matrix, [S], is not possible and two approximations are considered and compared-the transpose and the pseudo inverse sensitivity matrices.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
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