A hybrid segmentation method based on Gaussian kernel fuzzy clustering and region based active contour model for ultrasound medical images. (February 2015)

• Record Type: Journal Article
• Authors: Gupta, Deep; Anand, R.S.; Tyagi, Barjeev
• Is Part Of: Biomedical signal processing and control. Volume 16(2015)
• Page Start: 98
• Page End: 112
• Publication Date: 2015-02
• Subjects: Ultrasound -- Region based active contour model -- Gaussian kernel fuzzy C-means -- Segmentation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2014.09.013
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper presents a hybrid segmentation method for ultrasound medical images. The proposed method is based on the Gaussian kernel fuzzy clustering and active contour model driven by the region scalable fitting energy function. The proposed method provides approx 95% higher segmentation accuracy compared to others. It also gains the higher value of other parameters such as TP, FP, JSI, and DC. The proposed method helps to remove the need of manual intervention and also increase the averaged computational time. Abstract: Segmentation is a very crucial task for the ultrasound medical images due to the presence of various imaging artifacts and noise. This paper presents a hybrid segmentation method for the ultrasound medical images that utilize both the features of the Gaussian kernel induced fuzzy C-means (GKFCM) clustering and active contour model driven by region scalable fitting (RSF) energy function. In this method, the result obtained from the GKFCM method is utilized to initialize the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters used in the curve evolution process. The RSF formulation that is responsible for attracting the contour toward the object boundaries removes the requirement of the re-initialization process. The performance of the proposed method is evaluated by conducting several experiments on both the synthetic and real ultrasound images. Experimental results demonstrate that the proposed method produces better results by successfully detecting the object boundaries and also ensures an improvement in segmentation accuracy compared to others.