Texture Classification Using Neural Networks and Local Granulometries

This paper presents a method for segmenting interstitium and tubules in images of kidneys’ biopsies. Openings by structuring elements of increasing size, forming a granulometry, were performed on the entire image. For every pixel x and for each size of the structuring element the volume over a small window centered at x was measured (a local Granulometry). The vectors defined as the volume gradient served as an entry to a neural network (NN). The NN was taught to discriminate between vectors corresponding to pixels of the interstitium (textured region) and vectors corresponding to pixels of the tubules (non-textured region). The correlation factor between the area of the interstitium and the renal function was computed and compared to the results obtained with the manual procedure and two other automatic procedures.

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