An Open-source Image Analysis Toolbox for Quantitative Retinal Optical Coherence Tomography Angiography

Background: Qualitative and quantitative assessment of retinal perfusion using optical coherence tomography angiography (OCTA) has shown to be effective in the treatment and management of various retinal and optic nerve diseases. However, manual analyses of OCTA images to calculate metrics related to Foveal Avascular Zone (FAZ) morphology, and retinal vascular density and morphology are costly, time-consuming, subject to human error, and are exposed to both inter and intra operator variability.Objective: This study aimed to develop an open-source software framework for quantitative OCTA (QOCTA). Particularly, for analyzing OCTA images and measuring several indices describing microvascular morphology, vessel morphology, and FAZ morphology.Material and Methods: In this analytical study, we developed a toolbox or QOCTA using image processing algorithms provided in MATLAB. The software automatically determines FAZ and measures several parameters related to both size and shape of FAZ including area, perimeter, Feret’s diameter circularity, axial ratio, roundness, and solidity. The microvascular structure is derived from the processed image to estimate the vessel density (VD). To assess the reliability of the software, three independent operators measured the mentioned parameters for the eyes of 21 subjects. The consistency of the values was assessed using the intraclass correlation coefficient (ICC) index.Results: Excellent consistency was observed between the measurements completed for the superficial layer, ICC >0.9. For the deep layer, good reliability in the measurements was achieved, ICC >0.7. Conclusion: The developed software is reliable; hence, it can facilitate quantitative OCTA, further statistical comparison in cohort OCTA studies, and can assist with obtaining deeper insights into retinal variations in various populations

Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impact on the performance of a decomposition system. EMG decomposition has been studied well and several systems were proposed, but feature extraction step has not been investigated in detail. Objective: Several EMG signals were generated using a physiologically-based EMG signal simulation algorithm. For each signal, the firing patterns of motor units (MUs) provided by the simulator were used to extract MUPs of each MU. For feature extraction, different wavelet families including Daubechies (db), Symlets, Coiflets, bi-orthogonal, reverse bi-orthogonal and discrete Meyer were investigated. Moreover, the possibility of reducing the dimensionality of MUP feature vector is explored in this work. The MUPs represented using wavelet-domain features are transformed into a new coordinate system using Principal Component Analysis (PCA). The features were evaluated regarding their capability in discriminating MUPs of individual MUs. Results: Extensive studies on different mother wavelet functions revealed that db2, coif1, sym5, bior2.2, bior4.4, and rbior2.2 are the best ones in differentiating MUPs of different MUs. The best results were achieved at the 4th detail coefficient. Overall, rbior2.2 outperformed all wavelet functions studied; nevertheless for EMG signals composed of more than 12 MUPTs, syms5 wavelet function is the best function. Applying PCA slightly enhanced the results

A machine learning-based system for detecting leishmaniasis in microscopic images

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65 recall and 50 precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52 and 71, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods