IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 05 Issue: 01 | Jan-2016, Available @ http://www.ijret.org 245 LIVER EXTRACTION USING HISTOGRAM AND MORPHOLOGY S. Kiruthika 1 , I. Kaspar Raj 2 1 Women Scientist, Computer Centre, Gandhigram Rural Institute-Deemed University, Tamilnadu 1 kiruthikasm09@gmail.com 2 Director i/c, Computer Centre, Gandhigram Rural Institute-Deemed University, Tamilnadu 2 kasparraj@gmail.com Abstract Liver is the largest glandular organ important for survival in human body. Computed tomography is generally used to image liver due to its precision. This paper presents a method to extract liver from computed tomography (CT) abdomen images in axial orientation. A traditional segmentation method based on histogram and morphology is proposed herein. Histogram is used to analyze the intensity distribution, morphological operations are used to disconnect liver from the neighboring organs and greatest connected pixels are extracted. The experimental results of the proposed method when applied to CT abdomen images with contrast are presented and the effectiveness is discussed in accordance to the manual tracing obtained from the radiologist. Dice similarity co-efficient amounts to 94% in the proposed method. Keywords: Segmentation, Extraction, Histogram, Morphology, Connected Component, CT Liver ---------------------------------------------------------------------***--------------------------------------------------------------------- 1. INTRODUCTION Liver is one of the most important organs in the human body. It carries out varieties of functions such as filtering the blood, making bile and proteins, processing sugar, hormone production, breaking down medications and storing iron, vitamins and minerals[1]. Liver weights approximately 1500g, and is located in the upper right corner of the abdomen. Liver is the largest organ in the abdominal cavity. Liver disease is one of the most serious health diseases that cause death worldwide [2]. In modern medicine, medical imaging techniques has major role in helping diagnosis. Medical imaging is the technique and process of creating visual representation of the function of some organs or tissues. Various types of imaging technologies based on non-invasive approach are X-rays, Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI) scan, ultrasound, Positron Emission Tomography (PET) etc. These imaging technologies are used to view the human body in order to diagnose, monitor, or treat medical conditions. Each type of technology gives different information about the body being studied or treated. Liver image segmentation has played a very important role in medical imaging field. The advancement in digital image processing techniques has attracted researchers towards the development of computerized methods for liver analysis. Liver image segmentation is an essential step for the diagnosis of liver tumors, liver surgical planning system such as a system for liver transplantation and 3D liver volume rendering. Computed Tomography image has been widely used for liver disease diagnosis. Computed Tomography images can be taken either with contrast agent given to the patients or without contrast agent according to the necessity. The most challenging task in segmenting liver from the CT abdomen image is highly varying shape of the liver, weak boundaries to its adjacent organs such as stomach, kidney, and heart and the intensity homogeneity between adjacent organs [3]. The axial section of computed tomography (CT) of the abdomen image is shown in Fig-1. Fig-1: CT scan of Abdomen Image (I R ) (A)Liver; (B)Stomach; (C)Spleen; (D)Spine; (E) Aorta; (F) Fat Prior knowledge about location of the organ and image features is required before segmenting liver from CT abdomen images. The extraction of liver region from CT abdomen images using histogram analysis and morphological operations is proposed in this paper. This paper is organized as follows: various approaches used for liver segmentation is reviewed in section 2. Materials used in this method are given in section 3. Section 4 A B C D E F