Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Segmentation of Articular Cartilage and Early Osteoarthritis based on the Fuzzy Soft Thresholding Approach Driven by Modified Evolutionary ABC Optimization and Local Statistical Aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel’s classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel’s membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise

Design and Implementation of Selected Evolution Strategies for Optimization of Regional Segmentation Models with the Aim of Objects Identification from Medical Images

Tématem této diplomové práce je testování efektivity segmentačních algoritmů při segmentaci medicínských obrazových dat, jejichž akvizice byla provedena pomocí MRI, fundus kamery a ultrazvuku. V druhé části práce týkající se segmentace vybraných objektů zájmu byly použity snímky CT, MRI a ultrazvuku. Šum v obraze představuje nežádoucí aditivní složku, která mění jasovou intenzitu pixelů, a mohou tak při klasifikaci pixelů do jednotlivých segmentačních regionů vznikat chyby. V práci byla pro testování medicínských snímků dvojice použita dvojice algoritmů Fuzzy-ABC a F-FCM, které stojí na principu fuzzy logiky a jsou doplněny o lokální statistickou agregaci pro účely potlačení vlivu šumu. Další dvojicí algoritmů představují metody K-means a Otsu prahování. Tyto dva algoritmy se řadí mezi tzv. konvenční algoritmy a jejich segmentační efektivita byla porovnána s efektivitou obou fuzzy algoritmů. Teoretická část práce je stručně věnována základním principům segmentace obrazových dat a vybraných evolučních strategií pro segmentaci obrazu. Byla provedena také rešerše týkající se segmentace obrazu optimalizované pomocí evolučních strategií. Hlavním cílem práce byla analýza efektivity a robustnosti segmentačních metod v kontextu variabilního deterministického šumu s dynamickou intenzitou a následná komparativní analýza a modelování efektivity segmentace testovaných metod v závislosti na parametrech segmentačních strategiích. Testovány byly obrazy obsahující Gaussovský šum, Salt&Pepper a Speckle. K evaluaci výsledků byly použity objektivní evaluační metody MSE, korelace a SSIM.The topic of this diploma thesis is testing the effectiveness of segmentation algorithms in the segmentation of medical image data, the acquisition of which was performed using MRI, fundus camera and ultrasound. In the second part of the work dealing with the segmentation of selected objects of interest, CT, MRI and ultrasound images were used. Noise in the image is an undesirable additive component that changes the brightness intensity of the pixels, and thus errors can occur when classifying pixels into individual segmentation regions. A pair of Fuzzy-ABC and F-FCM algorithms, which are based on the principle of fuzzy logic, were tested in this work. These algorithms overcome the problem of pixel misclassification caused by local statistical aggregation. Another pair of algorithms are the K-means and Otsu thresholding methods. These two algorithms are so-called conventional algorithms, and their segmentation efficiency was compared with the efficiency of both fuzzy algorithms. The theoretical part of the work is briefly devoted to the basic principles of image data segmentation and selected evolutionary strategies for image segmentation. A review of such evolutionary strategies used for image segmentation was also made. The main goal of the work was to analyze the effectiveness and robustness of segmentation methods in the context of variable deterministic noise (gaussian, salt&pepper, speckle) with dynamic intensity and subsequent comparative analysis and modeling of the effectiveness of segmentation of tested methods depending on the parameters of segmentation strategies. Objective evaluation methods were used to evaluate the results (corelation, MSE and SSIM).450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn