A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images. (23rd May 2023)

• Record Type: Journal Article
• Authors: Sun, Jinhong; Qi, Liang; Song, Yinglei; Qu, Junfeng; Khosravi, Mohammad R.
• Is Part Of: International journal of nanotechnology. Volume 20:Number 1/4(2023)
• Page Start: 75
• Page End: 97
• Publication Date: 2023-05-23
• Subjects: medical image retrieval -- similarity -- alignment of histograms -- dynamic programming -- intensity features -- textual features; 620.505
• Languages: English
• ISSNs: 1475-7435

Abstract

Recently, with the explosive growth in the number of available medical images generated by medical imaging systems, content-based retrieval of medical images has become an important method for the diagnosis and study of many diseases. Most existing methods find medical images similar to a given one based on the extraction and comparison of crucial image features. However, similarity values computed with low level visual features of an image generally do not match the similarity obtained from human observation well. The overall performance of these methods is thus often unsatisfactory. This paper proposes a dynamic programming approach for content-based retrieval of medical images. The approach represents an image with three different histograms that contain both crucial intensity and textural features of the image. The similarity between two images is evaluated with a dynamic programming approach that can optimally align the peaks in the corresponding histograms from both images. Experiments show that the proposed approach is able to generate retrieval results with high accuracy. A comparison with state-of-the-art approaches for content-based medical image retrieval shows that the proposed approach can achieve higher retrieval accuracy in both ordinary and nano-scale medical images. As a result, higher retrieval accuracy may lead to more reliable results for the diagnosis and treatment of many diseases. The proposed approach is thus potentially useful for improving the reliability of many applications in health informatics.