Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.
Certain skin diseases are chronic, inflammatory and without cure. However, there are many treatment options that can
clear them for a period of time. Measuring their severity and assessing their extent, is a fundamental issue to determine
the efficacy of the treatment under test. Two of the most important parameters of severity assessment are Erythema
(redness) and Scaliness. Physicians classify these parameters into several grades by visual grading method. In this paper
a color image segmentation and classification algorithm is developed to obtain an assessment of erythema and scaliness
of dermatological lesions. Color digital photographs taken under an acquisition protocol form the database. Difference
between green band and blue band of images in RGB color space shows two modes (healthy skin and lesion) with clear
separation. Otsu's method is applied to this difference in order to isolate the lesion. After the skin disease is segmented,
some color and texture features are calculated and they are the inputs to a Fuzzy-ARTMAP neural network. The neural
network classifies them into the five grades of erythema and the five grades of scaliness. The method has been tested
with 31 images with a success percentage of 83.87 % when the images are classified in erythema, and 77.42 % for
scaliness classification.
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