Automatic whole-body bone scan image segmentation based on constrained local model

Author:

Rachmawati Ema,Jondri Jondri,Ramadhani Kurniawan Nur,Kartamihardja Achmad Hussein Sundawa,Achmad Arifudin,Shintawati Rini

Abstract

In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.

Publisher

Institute of Advanced Engineering and Science

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Instrumentation,Information Systems,Control and Systems Engineering,Computer Science (miscellaneous)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Leveraging Model Scaling and Butterfly Network in the Bone Scan Image Segmentation;International Journal of Computational Intelligence Systems;2024-04-11

2. Whole-Body Bone Scan Segmentation Using SegFormer;2023 10th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE);2023-08-31

3. Investigating Convolution-Attention Model for Bone Scan Image Segmentation;2023 10th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE);2023-08-31

4. Semantic Segmentation of Whole-Body Bone Scan Image Using Btrfly-Net;2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE);2022-10-18

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