Role of Segmentation in Medical Imaging: A Comparative Study

Abstract

Rapid advances in the field of medical imaging are revolutionizing medicine. The determination of the presence or severity of disease will impact clinical care for a patient or outcome status in a study. The use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of lesion detection has become a focus of medical imaging and diagnostic radiology research. Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. In this paper, segmentation problems in medical imaging modalities especially for lung CT as well as for thyroid ultrasound (US) are discussed along with their comparative results are shown using automatic tools as well as with some specific algorithms. Various automatic tools have been used and discussed. The results shows that though segmentation is the crucial, required and most difficult phase yet the outcome is really advantageous in medicine for the perfect diagnosis of any disease. Both the outcomes either from automatic tool as well as using an algorithm provide the required ROI (region of interest).

Segmentation And Analysis Of Interstitial Lung Disease Pattern In HRCT Images And Disease Classification By K- Means

“…Despite intensive research, segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture, and other factors. There are many algorithms and techniques available for image segmentation but still, there needs to develop an efficient, fast technique of medical image segmentation [5]. The goal of segmentation is to change the representation of an image to be more meaningful and easier to analyze.…”

Implementation of a Fused Approach for Segmentation of Brain MR Images for Tumor Extraction

“…Both MRI and CT scan use computers to create detailed images of the brain. The other methods include PET (Positron Emission Tomography) scan, DTI ( Diffusion Tensor Imaging ), MRS (Magnetic Resonance Spectroscopy) scan and biopsy (tissue sample analysis) [6][7].…”

Analysis of Watermarking Techniques for Medical Images Preserving ROI

“…Segmentation plays an important role in medical image processing [4,5]. In medical image analysis segmentation is the first step to be followed, to avoid distortion of ROI [4,6].…”

“…In medical image analysis segmentation is the first step to be followed, to avoid distortion of ROI [4,6]. Image segmentation deals with the process of partitioning an image into different regions by grouping together neighborhood pixels based on some predefined similarity criterion [7].…”