IEEE J Biomed Health Inform - Computer-aided diagnosis in hysteroscopic imaging.

Tópicos

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Resumo

The paper presents the development of a computer-aided diagnostic (CAD) system for the early detection of endometrial cancer. The proposed CAD system supports reproducibility through texture feature standardization, standardized multifeature selection, and provides physicians with comparative distributions of the extracted texture features. The CAD system was validated using 516 regions of interest (ROIs) extracted from 52 subjects. The ROIs were equally distributed among normal and abnormal cases. To support reproducibility, the RGB images were first gamma corrected and then converted into HSV and YCrCb. From each channel of the gamma-corrected YCrCb, HSV, and RGB color systems, we extracted the following texture features: 1) statistical features (SFs), 2) spatial gray-level dependence matrices (SGLDM), and 3) gray-level difference statistics (GLDS). The texture features were then used as inputs with support vector machines (SVMs) and the probabilistic neural network (PNN) classifiers. After accounting for multiple comparisons, texture features extracted from abnormal ROIs were found to be significantly different than texture features extracted from normal ROIs. Compared to texture features extracted from normal ROIs, abnormal ROIs were characterized by lower image intensity, while variance, entropy, and contrast gave higher values. In terms of ROI classification, the best results were achieved by using SF and GLDS features with an SVM classifier. For this combination, the proposed CAD system achieved an 81% correct classification rate.

Resumo Limpo

paper present develop computeraid diagnost cad system earli detect endometri cancer propos cad system support reproduc textur featur standard standard multifeatur select provid physician compar distribut extract textur featur cad system valid use region interest roi extract subject roi equal distribut among normal abnorm case support reproduc rgb imag first gamma correct convert hsv ycrcb channel gammacorrect ycrcb hsv rgb color system extract follow textur featur statist featur sfs spatial graylevel depend matric sgldm graylevel differ statist glds textur featur use input support vector machin svms probabilist neural network pnn classifi account multipl comparison textur featur extract abnorm roi found signific differ textur featur extract normal roi compar textur featur extract normal roi abnorm roi character lower imag intens varianc entropi contrast gave higher valu term roi classif best result achiev use sf glds featur svm classifi combin propos cad system achiev correct classif rate

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