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In this paper, it is intended to enhance the simple region growing technique (RG) to extract liver from the abdomen away from other organs in CT images. Iterative K-means clustering technique is used as a preprocessing step to pass the image to region growing and watershed segmentation techniques. The usage of K-means and region growing is preferred here for its simplicity and low cost of execution...
In this paper, we present an image segmentation technique based on fuzzy c-means (FCM) incorporated with wavelet domain noise filtration. With the use of image noise feature estimation composed of preliminary coefficient classification and wavelet domain indicator, a filter for balancing the preservation of relevant details against the degree of noise reduction can be created. The filter is further...
In this paper, an automated liver CT image clustering approach based on evolutionary metaheuristic algorithm called invasive weed optimization is presented without any prior information about the number of naturally occurring groups in the images. The fitness function used in the genetic algorithm is k-means objective function for searching of the smoothed compact cluster. The experimental results...
This article introduces hybrid automatic liver Parenchyma segmentation approach from abdominal CT images. The proposed approach consist of four main phases. Firstly, preprocessing phase which converts CT image into binary image using adaptive threshold method that examine the intensity values of the local neighborhood of each pixel. Then, the second phase is to apply multi-scale morphological operators...
Abnormalities in the liver include masses which can be benign or malignant. Due to the presence of these abnormalities, the regularity of the liver structure is altered, which changes its fractal dimension. In this paper, we present a computer aided diagnostic system for classifying liver abnormalities from abdominal CT images using fractal dimension features. We integrate different methods for liver...
Liver cancer is one of the major death factors in the world. Transplantation and tumor resection are two main therapies in common clinical practice. Both tasks need image assisted planning and quantitative evaluations. An efficient and effective automatic liver segmentation is required for corresponding quantitative evaluations. Computed Tomography (CT) is highly accurate for liver cancer diagnosis...
Manual segmentation of liver computerized tomography (CT) images is very time consuming, so it is desired to develop a computer-based approach for the analysis of liver CT images that can precisely segment the liver without any human intervention. This paper presents normalized cuts graph partitioning approach for liver segmentation from CT images. To evaluate the performance of the presented approach,...
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