Fuzzy C-Means-Driven Fhce Contextual Segmentation Method For Mammographic Microcalcification Detection

The frequency histogram of connected elements (FHCE) is a recently proposed algorithm that has successfully been applied in various medical image segmentation tasks. The FHCE is based on the idea that most pixels belong to the same class as their neighbouring pixels. However, the FHCE performance relies to a great extent on the optimal selection of a threshold parameter. Since evaluating segmentation results is a highly subjective process, a collection of threshold values must typically be examined. No algorithm has been proposed to automate the determination of the threshold parameter value of the FHCE. This study presents a method based on the fuzzy C-means clustering algorithm, designed to automatically generate optimal threshold values for the FHCE. This new approach was applied as a part of a structured sequence of image processing steps in order to facilitate segmentation of microcalcifications in digitized mammograms. A unique threshold value was generated for each mammogram, taking into account the different grey-level patterns based on different compositions of various breast tissues in it. The segmentation algorithm was tested on 100 mammograms (50 collected from the Mammographic Image Analysis Society and 50 normal mammograms onto which a number of simulated microcalcifications were generated). The algorithm was able to detect subtle microcalcifications with sensitivity ranging from 93 to 98%, False alarm ratio from 3 to 5% and false negatives variability from 2 to 3%.