NEUTROSOPHIC TEXTURE-REGION DIFFERENCE-BASED FUZZY c-MEANS CLUSTERING OF ULTRASOUND TUMOR IMAGES

Abstract

Owing to its cost effective and non-ionizing procedure, ultrasound imaging has become a primary tool for detection and diagnosis of breast tumors compared to mammography or biopsy. But the precise interpretation of the images obtained is limited due to the intensity inhomogeneties, speckle noise, low contrast, etc. present in the observed images. Also, tumors can appear in different echogenic patterns including visually challenging to detect isoechoic and slightly hypoechoic tumors, and distinguishing the tumor by means of intensity variations only will not suffice. Therefore it is important to preprocess the image to remove unwanted noise and variations, and thereby provide adequate enhancement to highlight the region of interest. In this paper, a segmentation method is proposed where the query images are clustered using fuzzy c-means clustering (FCM) after neutrosophic filtering and enhancement followed by extraction of textural and edge features through the use of Gabor and compass filters. In this work, Prewitt compass filters were implemented to capture the maximum region difference between the tumor and background. By using filters of different orientations, sufficient texture and edge information were able to be captured by using the combination of Gabor and compass filters, which can then aid in the accurate detection of isoechoic and hypoechoic tumors. The neutrosophic preprocessing implemented in this work models the ultrasound noise as combination of Gaussian and Rayleigh distributions, and shows better signal-to-noise ratio than traditionally modeling speckle as Gaussian alone. The proposed method gave an increase of 42.25%, 40.15%, 81.32% and 50.15% and a decrease of 95.83% compared to standard Otsu thresholding, an increase of 23.34%, 19.57%, 24.53% and 24.71% and a decrease of 91.81% compared to simple FCM and an increase of 37.89%, 44.64%, 35.86% and 48.11% and a decrease of 93.68% compared to active contour segmentation in structural similarity index, Jaccard index, Dice coefficient and accuracy and mean square error, respectively. On contrasting with recent learning-based methodologies for tumor segmentation, the proposed method gave comparable accuracy without the need for large training database and expensive graphical processing unit (GPU) computing capabilities.