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Robust 3D Texture Classifier using Score Block Operations


Mira Park


Vol. 6  No. 9  pp. 175-184


This paper presents a scheme of analyzing lung texture to solve the problem of identifying undefined patterns and distinguishing the complex background of superimposed structures in chest radiographs. The method detects and quantifies the interstitial abnormalities in a chest radiograph using the contents based image retrieval (CBIR) scheme. This technique is based on the image feature vector obtained from quasi-Gabor filters and a 3D structure classification scheme. The quasi-Gabor filters are capable of maintaining low computational cost while keeping the important information of the power spectrum of 2D-DFFT, such as band-pass frequency and direction of texture. The 3D classifier is able to capture not only local texture but also global distribution of lung texture and it overcomes the existing problems of a general block operation such as sliding block operation. Our method is a generic one in the sense that it could be applied to analyze the texture of any other images including natural scenery and various medical images such as CT or MRI images


Chest radiograph, texture, score block operation.