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
Keywords:
Biomedical image classification, SIFT features, statistical features, mutual dependence, maximum likelihood, dynamic programming.
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.
