Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming

Title:Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming

Volume: 3

Author(s): Xinpeng Man and Yinglei Song*

Affiliation:

• School of Automation, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

Abstract: Background: In the research and practice of medical sciences, accurate classification of biomedical images with computer programs may provide an important basis for the study and diagnosis of many diseases.

Methods: This paper proposes a new statistical approach that can accurately classify biomedical images based on their statistical features. In the first step of the proposed approach, a number of SIFT features of different types are computed for each pixel in a biomedical image and a statistical feature that describes the distribution of each type of SIFT features is obtained for the image. In the second step, a dynamic programming approach is used to efficiently analyze the dependence among different statistical features associated with an image and compute the probability for an image to belong to each possible class; the class with the largest probability is determined as the result of classification.

Results: Experimental results show that the proposed approach can lead to classification results with accuracy higher than that of a few state-of-the-art approaches for the classification of biomedical images.

Conclusion: The proposed approach can achieve classification accuracy comparable to that of several state-of-the-art classification approaches. It is thus potentially useful for applications where large models are not appropriate for classification tasks due to limitations in computational or communication resources.

Cite this article as:

Man Xinpeng and Song Yinglei*, Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming, Current Computer Science 2024; 3 : e280524230384 . https://dx.doi.org/10.2174/0129503779291012240424070357