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Relationship of maxillary incisors in complete dentures to the incisive papilla

Isa ZM, Abdulhadi LM

J Oral Sci, 2012;54(2):159-63.
PMID: 22790408

We investigated the relationship of the maxillary central incisors to the incisive papilla in wearers of complete dentures. First, image analyzer software was used to examine the relationship of the midpoint of the incisive papilla to the labial surface of the maxillary central incisors on occlusal photographs of 120 maxillary casts from dentate Malaysian adults. Then, an Alma denture gauge was used to identify the position of the labial surface of the maxillary central incisors in relation to the midpoint of the incisive papilla in complete dentures from 51 patients who requested replacement dentures at the Faculty of Dentistry, University of Malaya. The mean incisor distance to the incisive papilla in dentate adults was 9.59 ± 1.00 mm, while the mean incisor distance to the incisive papilla in complete dentures was 6.34 ± 1.87 mm. Thus, in our sample of edentulous patients, the anterior teeth in complete dentures were positioned approximately 3 mm closer to the incisive papilla, as compared with the position of the central incisors in natural dentition, and did not duplicate the position of the natural anterior teeth.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Locating abnormalities in brain blood vessels using parallel computing architecture

Adeshina AM, Hashim R, Khalid NE, Abidin SZ

Interdiscip Sci, 2012 Sep;4(3):161-72.
PMID: 23292689 DOI: 10.1007/s12539-012-0132-y

CT and MRI scans are widely used in medical diagnosis procedures, but they only produce 2-D images. However, the human anatomical structure, the abnormalities, tumors, tissues and organs are in 3-D. 2-D images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require doctors to use their expert experiences in the interpretation of the possible location, size or shape of the abnormalities, even for large datasets of enormous amount of slices. Previously, the concept of reconstructing 2-D images to 3-D was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This paper proposes a volume visualization framework using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft.NET environment for easy interoperability with other emerging revolutionary tools. The framework was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing around 109 MRA datasets. Uniquely, at a reasonably cheaper cost, our framework achieves immediate reconstruction and obvious mappings of the internal features of human brain, reliable enough for instantaneous locations of possible blockages in the brain blood vessels.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Investigation of CPD and HMDS sample preparation techniques for cervical cells in developing computer-aided screening system based on FE-SEM/EDX

Jusman Y, Ng SC, Abu Osman NA

ScientificWorldJournal, 2014;2014:289817.
PMID: 25610902 DOI: 10.1155/2014/289817

This paper investigated the effects of critical-point drying (CPD) and hexamethyldisilazane (HMDS) sample preparation techniques for cervical cells on field emission scanning electron microscopy and energy dispersive X-ray (FE-SEM/EDX). We investigated the visualization of cervical cell image and elemental distribution on the cervical cell for two techniques of sample preparation. Using FE-SEM/EDX, the cervical cell images are captured and the cell element compositions are extracted for both sample preparation techniques. Cervical cell image quality, elemental composition, and processing time are considered for comparison of performances. Qualitatively, FE-SEM image based on HMDS preparation technique has better image quality than CPD technique in terms of degree of spread cell on the specimen and morphologic signs of cell deteriorations (i.e., existence of plate and pellet drying artifacts and membrane blebs). Quantitatively, with mapping and line scanning EDX analysis, carbon and oxygen element compositions in HMDS technique were higher than the CPD technique in terms of weight percentages. The HMDS technique has shorter processing time than the CPD technique. The results indicate that FE-SEM imaging, elemental composition, and processing time for sample preparation with the HMDS technique were better than CPD technique for cervical cell preparation technique for developing computer-aided screening system.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Intelligent screening systems for cervical cancer

Jusman Y, Ng SC, Abu Osman NA

ScientificWorldJournal, 2014;2014:810368.
PMID: 24955419 DOI: 10.1155/2014/810368

Advent of medical image digitalization leads to image processing and computer-aided diagnosis systems in numerous clinical applications. These technologies could be used to automatically diagnose patient or serve as second opinion to pathologists. This paper briefly reviews cervical screening techniques, advantages, and disadvantages. The digital data of the screening techniques are used as data for the computer screening system as replaced in the expert analysis. Four stages of the computer system are enhancement, features extraction, feature selection, and classification reviewed in detail. The computer system based on cytology data and electromagnetic spectra data achieved better accuracy than other data.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
A discrimination method for the detection of pneumonia using chest radiograph

Noor NM, Rijal OM, Yunus A, Abu-Bakar SA

Comput Med Imaging Graph, 2010 Mar;34(2):160-6.
PMID: 19758785 DOI: 10.1016/j.compmedimag.2009.08.005

This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q(2). The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15.

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
Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors

Vicnesh J, Wei JKE, Ciaccio EJ, Oh SL, Bhagat G, Lewis SK, et al.

J Med Syst, 2019 Apr 26;43(6):157.
PMID: 31028562 DOI: 10.1007/s10916-019-1285-6

Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

Raghavendra U, Gudigar A, Maithri M, Gertych A, Meiburger KM, Yeong CH, et al.

Comput Biol Med, 2018 04 01;95:55-62.
PMID: 29455080 DOI: 10.1016/j.compbiomed.2018.02.002

Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

Wan Ahmad WS, Zaki WM, Ahmad Fauzi MF

Biomed Eng Online, 2015;14:20.
PMID: 25889188 DOI: 10.1186/s12938-015-0014-8

Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.

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*
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*
Fulltext Robust video content analysis schemes for human action recognition

Aly CA, Abas FS, Ann GH

Sci Prog, 2021;104(2):368504211005480.
PMID: 33913378 DOI: 10.1177/00368504211005480

INTRODUCTION: Action recognition is a challenging time series classification task that has received much attention in the recent past due to its importance in critical applications, such as surveillance, visual behavior study, topic discovery, security, and content retrieval.
OBJECTIVES: The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms.
METHODS: This paper presents three robust action recognition techniques based on a series of image analysis methods to detect activities in different scenes. The general scheme architecture consists of shot boundary detection, shot frame rate re-sampling, and compact feature vector extraction. This process is achieved by emphasizing variations and extracting strong patterns in feature vectors before classification.
RESULTS: The proposed schemes are tested on datasets with cluttered backgrounds, low- or high-resolution videos, different viewpoints, and different camera motion conditions, namely, the Hollywood-2, KTH, UCF11 (YouTube actions), and Weizmann datasets. The proposed schemes resulted in highly accurate video analysis results compared to those of other works based on four widely used datasets. The First, Second, and Third Schemes provides recognition accuracies of 57.8%, 73.6%, and 52.0% on Hollywood2, 94.5%, 97.0%, and 59.3% on KTH, 94.5%, 95.6%, and 94.2% on UCF11, and 98.9%, 97.8% and 100% on Weizmann.
CONCLUSION: Each of the proposed schemes provides high recognition accuracy compared to other state-of-art methods. Especially, the Second Scheme as it gives excellent comparable results to other benchmarked approaches.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier

Islam MS, Hannan MA, Basri H, Hussain A, Arebey M

Waste Manag, 2014 Feb;34(2):281-90.
PMID: 24238802 DOI: 10.1016/j.wasman.2013.10.030

The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Analysis of semi-automated method for femur length measurement from foetal ultrasound

Rahmatullah B, Besar R

J Med Eng Technol, 2009;33(6):417-25.
PMID: 19637083 DOI: 10.1080/03091900802451232

The motivation of this paper is to analyse the efficiency and reliability of our proposed algorithm of femur length (FL) measurement for the estimation of gestational age. The automated methods are divided into the following components: threshold, segmentation and extraction. Each component is examined, and improvements are made with the objective of finding the optimal result for FL measurement. The methods are tested with a total of 200 different digitized ultrasound images from our database collection. Overall, the study shows that the watershed-based segmentation method combined with enhanced femur extraction algorithm and a 12 x 12 block averaging seed-point threshold method perform identically well with the expert measurements for every image tested and superior as compared to a previous method.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Harmonic phase interference for the detection of tag line crossings and beyond in homogeneous strain analysis of cardiac tagged MRI data

Bilgen M

Australas Phys Eng Sci Med, 2010 Dec;33(4):357-66.
PMID: 21110236 DOI: 10.1007/s13246-010-0039-z

Homogenous strain analysis (HSA) was developed to evaluate regional cardiac function using tagged cine magnetic resonance images of heart. Current cardiac applications of HSA are however limited in accurately detecting tag intersections within the myocardial wall, producing consistent triangulation of tag cells throughout the image series and achieving optimal spatial resolution due to the large size of the triangles. To address these issues, this article introduces a harmonic phase (HARP) interference method. In principle, as in the standard HARP analysis, the method uses harmonic phases associated with the two of the four fundamental peaks in the spectrum of a tagged image. However, the phase associated with each peak is wrapped when estimated digitally. This article shows that special combination of wrapped phases results in an image with unique intensity pattern that can be exploited to automatically detect tag intersections and to produce reliable triangulation with regularly organized partitioning of the mesh for HSA. In addition, the method offers new opportunities and freedom for evaluating myocardial function when the power and angle of the complex filtered spectra are mathematically modified prior to computing the phase. For example, the triangular elements can be shifted spatially by changing the angle and/or their sizes can be reduced by changing the power. Interference patterns obtained under a variety of power and angle conditions were presented and specific features observed in the results were explained. Together, the advanced processing capabilities increase the power of HSA by making the analysis less prone to errors from human interactions. It also allows strain measurements at higher spatial resolution and multi-scale, thereby improving the display methods for better interpretation of the analysis results.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext A Fuzzy-C-Means-Clustering Approach: Quantifying Chromatin Pattern of Non-Neoplastic Cervical Squamous Cells

Tang JR, Mat Isa NA, Ch'ng ES

PLoS One, 2015;10(11):e0142830.
PMID: 26560331 DOI: 10.1371/journal.pone.0142830

Despite the effectiveness of Pap-smear test in reducing the mortality rate due to cervical cancer, the criteria of the reporting standard of the Pap-smear test are mostly qualitative in nature. This study addresses the issue on how to define the criteria in a more quantitative and definite term. A negative Pap-smear test result, i.e. negative for intraepithelial lesion or malignancy (NILM), is qualitatively defined to have evenly distributed, finely granular chromatin in the nuclei of cervical squamous cells. To quantify this chromatin pattern, this study employed Fuzzy C-Means clustering as the segmentation technique, enabling different degrees of chromatin segmentation to be performed on sample images of non-neoplastic squamous cells. From the simulation results, a model representing the chromatin distribution of non-neoplastic cervical squamous cell is constructed with the following quantitative characteristics: at the best representative sensitivity level 4 based on statistical analysis and human experts' feedbacks, a nucleus of non-neoplastic squamous cell has an average of 67 chromatins with a total area of 10.827 μm2; the average distance between the nearest chromatin pair is 0.508 μm and the average eccentricity of the chromatin is 0.47.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Fulltext An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)

Chai HY, Wee LK, Swee TT, Salleh ShH, Chea LY

Biomed Eng Online, 2011;10:87.
PMID: 21952080 DOI: 10.1186/1475-925X-10-87

Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Fulltext QuickCount®: a novel automated software for rapid cell detection and quantification

Tiong KH, Chang JK, Pathmanathan D, Hidayatullah Fadlullah MZ, Yee PS, Liew CS, et al.

Biotechniques, 2018 12;65(6):322-330.
PMID: 30477327 DOI: 10.2144/btn-2018-0072

We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland-Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.

Matched MeSH terms: Image Processing, Computer-Assisted/methods*
Fulltext A generative adversarial network for synthetization of regions of interest based on digital mammograms

Oyelade ON, Ezugwu AE, Almutairi MS, Saha AK, Abualigah L, Chiroma H

Sci Rep, 2022 Apr 13;12(1):6166.
PMID: 35418566 DOI: 10.1038/s41598-022-09929-9

Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training data, and skillful selection of hyperparameters. The application of deep learning models to medical image processing has yielded interesting performance, capable of correctly detecting abnormalities in medical digital images, making them surpass human physicians. However, advancing research in this domain largely relies on the availability of training datasets. These datasets are sometimes not publicly accessible, insufficient for training, and may also be characterized by a class imbalance among samples. As a result, inadequate training samples and difficulty in accessing new datasets for training deep learning models limit performance and research into new domains. Hence, generative adversarial networks (GANs) have been proposed to mediate this gap by synthesizing data similar to real sample images. However, we observed that benchmark datasets with regions of interest (ROIs) for characterizing abnormalities in breast cancer using digital mammography do not contain sufficient data with a fair distribution of all cases of abnormalities. For instance, the architectural distortion and breast asymmetry in digital mammograms are sparsely distributed across most publicly available datasets. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms. Our approach involves the design of a GAN model consisting of both a generator and a discriminator to learn a hierarchy of representations for abnormalities in digital mammograms. Attention is given to architectural distortion, asymmetry, mass, and microcalcification abnormalities so that training distinctively learns the features of each abnormality and generates sufficient images for each category. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. In addition, the quality of the images generated was also evaluated using PSNR, SSIM, FSIM, BRISQUE, PQUE, NIQUE, FID, and geometry scores. The results showed that ROImammoGAN performed competitively with state-of-the-art GANs. The outcome of this study is a model for augmenting CNN models with ROI-centric image samples for the characterization of abnormalities in breast images.

Matched MeSH terms: Image Processing, Computer-Assisted/methods
Fulltext Semantic representation of monogenean haptoral Bar image annotation

Abu A, Susan LL, Sidhu AS, Dhillon SK

BMC Bioinformatics, 2013;14:48.
PMID: 23398696 DOI: 10.1186/1471-2105-14-48

Digitised monogenean images are usually stored in file system directories in an unstructured manner. In this paper we propose a semantic representation of these images in the form of a Monogenean Haptoral Bar Image (MHBI) ontology, which are annotated with taxonomic classification, diagnostic hard part and image properties. The data we used are basically of the monogenean species found in fish, thus we built a simple Fish ontology to demonstrate how the host (fish) ontology can be linked to the MHBI ontology. This will enable linking of information from the monogenean ontology to the host species found in the fish ontology without changing the underlying schema for either of the ontologies.

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

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