Image Processing Segmentation Algorithms Evaluation through Implementation Choices

被引：0

作者：

Vizza, Patrizia ^{[1
]}

Cannistra, Mattia ^{[1
]}

Giancotti, Raffaele ^{[1
]}

Veltri, Pierangelo ^{[1
]}

机构：

[1] Magna Graecia Univ Catanzaro, Dept Surg & Med Sci, Catanzaro, Italy

来源：

13TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, BCB 2022 | 2022年

关键词：

k-means; image processing; segmentation;

D O I：

10.1145/3535508.3545593

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The processing of medical images is gaining an important role to allow an increasingly accurate diagnosis, essential for chronic diseases identification and treatment. We focus on image processing techniques, such as segmentation ones, and we report implementation experiences and tests in different programming languages. Results regard the use and implementation of K-means algorithm to analyze T1-weighted MRI images regarding 233 subjects. Dataset refers to on line available one containing images referred to three different brain tumors (meningioma, glioma and pituitary tumor). We report the results of implementing the K-means algorithm by using two different programming languages, Java and Octave, measuring different performances.

引用

页数：7

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