CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm.

Imaging the liver.

An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images

Liver diseases are considered seriously because liver is a vital organ to human beings. Computer aided liver analysis is a technique that can help radiologists to accurately identify diseases that can help in reducing the risk of liver surgery. The computer aided diagnosis (CAD) system consists of the segmentation of liver and lesion, extraction of features from a lesion and characterization of liver diseases by means of a classifier. In the last decade, the use of many segmentation techniques and classifier systems have been proposed by many authors with the intention to increase the performance of CAD systems This article focuses on various textural analysis methods used so far for the classification of liver diseases from abdominal Computed Tomography scans. It reviews the techniques and results of the various methods are analyzed and summarized. The future direction for the research is also discussed

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A Study of Textural Analysis Methods for the Diagnosis of Liver Diseases from Abdominal Computed Tomography

In this day and age, digital images play a significant role in our day-to-day life. Digital images are utilized in a wide range of fields like medical, business and more. Besides, the digital images play a vital part in the medical field in which it has been utilized to analyze the anatomy. These medical images are used in the identification of different diseases. Regrettably, the medical images have noises due to its different sources in which it has been produced. Confiscating such noises from the medical images is extremely crucial because these noises may degrade the quality of the images and also baffle the identification of the disease. Hence, denoising of medical images is indispensable. Researchers have recognized this issue and have provided lots of paradigms and techniques for use in the medical image denoising process. In this paper, an extensive review on denoising of medical images is presented together with the classification of medical images into either Radiographic or Ultrasound or MRI or CT image. In addition, a brief description on the digital images and medical images is presented. A concise note on Radiography, Ultrasound, MRI and CT images is also presented.

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An Extensive Review of Significant Researches on Medical Image Denoising Techniques

In this paper, a computer-aided diagnostic (CAD) system for the diagnosis of benign and malignant liver tumors from computed tomography (CT) images is presented. Liver is segmented from abdominal CT images using adaptive threshold and morphological processing. Each suspicious tumor region is automatically extracted from the segmented liver using FCM technique and the textural information obtained using curvelet transform is used to train the classifier and classify the tumors. Two kinds of liver diseases are identified: hemangioma and hepatocellular carcinoma. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.

R. S. Moni

Papers

Diagnosis of Liver Tumor from CT Images using Curvelet Transform