Penalized Fuzzy C-Means Enabled Hybrid Region Growing in Segmenting Medical Images

Abstract

Segmentation is considered as one of the challenging and important processes in the field of digital image processing, and there are numerous applications like medical image analysis and satellite data processing where the digital image processing can beneficial. Various algorithms have been developed by many researchers to analyze different medical images like MRI and X-rays. Nuclear image analysis and interpretation also a promising research topic in medical image analysis. For example, positron emission tomography (PET) image can be used to accurately localize disease to help doctors in providing the right treatment and saving valuable time. In recent years, there is a significant advancement in the biomedical imaging domain with the increasing accessibility of computational power as well as automated systems; medical image analysis has become one of the most interesting research areas. Microscopic image analysis is also valuable in the domain of medical imaging as well as medicine. For example, different cell determination, identification, and counting are an important and almost unavoidable step that assists to diagnose some precise diseases. Many computer vision and digital image analysis instances require a basic segmentation phase to find different objects or separate the test image into distinct segments, which can be treated homogeneous depending upon a given property, such as color and texture. Region growing and fuzzy C-Means are two efficient and popular segmentation techniques. In this chapter, we have proposed a hybrid scheme for image segmentation using fuzzy C-Means clustering, region growing method, and thresholding. The fuzzy C-Means (FCM) clustering is used as a preprocessing step. It helps to process the image more accurately in further stages. To eliminate the noise sensitivity property of fuzzy C-Means (FCM), we have used the PFCM, i.e., penalized FCM clustering algorithm. In this work, to find the appropriate region, we have used the region growing segmentation technique coupled with the thresholding. In the proposed work, a similarity feature depending on pixel intensity is used. The threshold value can be calculated using different techniques, such as iterative approach, Otsu’s technique, local thresholding, and manual selection, to determine the optimal threshold. The results are obtained and derived by applying the proposed method on different images obtained from publicly available benchmark datasets of human brain MRI images. Experimental results indicated that the PFCM supported hybrid model is far more superior in segmenting medical images than traditional image segmentation methods.