A Region-based Level Set Formulation Using Machine Learning Approach in Medical Image Segmentation

被引:0
|
作者
Biswas, Soumen [1 ]
Hazra, Ranjay [1 ]
Prasad, Shitala [2 ]
机构
[1] Natl Inst Technol Silchar, Dept Elect & Instrumentat, Silchar, Assam, India
[2] Nanyang Technol Univ, CYREN, Singapore, Singapore
来源
PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY | 2019年
关键词
Active Contour Model; k-nearest neighbor; Level Set; Medical Image Segmentation; ACTIVE CONTOURS DRIVEN;
D O I
10.1109/tencon.2019.8929350
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A new region-based active contour model in level set formulation is proposed to segment medical images with poorly defined boundaries. From literature, it is observed that the traditional methods often fail to detect weak boundaries for images with intensity inhomogeneity. However, the machine learning (ML) algorithms are highly effective for such images but due to the noise most pixels are misclassified. Therefore in this paper, we propose a region-based active contour model using ML. In this paper, we consider an active contour driven by local Gaussian distribution (LGD) fitting energy which is known as LGD model. Further, this active contour LGD model is integrated with fuzzy k-nearest neighbor (k-NN) for added accurate segmentation. Also the energy stop function (ESF) of LGD model is modified to combine with k-NN. The results obtained are compared with the existing state-of-the-art models and the proposed method is clearly a triumph. The experimental results proves that the proposed model provides higher accuracy results for medical image segmentation and is robust compared to the other existing methods.
引用
收藏
页码:470 / 475
页数:6
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