Medical X-ray Images Classification Based on Shape Features and Bayesian Rule

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

“…The relevant classes are class 05, class 07, class 08, class 09, class 11, class 19, class 21, and class 38, respectively. In Table 2, the scores of relevant classes are greater than the score threshold of 30 which these scores are obtained based on fusion of the dependency probabilities P m′,n (m′=5, 7,8,9,11,19,21,38 and n= 1,⋯,7) of 7 feature descriptors. The largest dependency probability is related to P 11,7 where is equal to 0.9781, and it means that conditional probability of query belonging to class 11 using the seventh feature descriptor (angular function FDs) is a b Figure 11 shows the retrieval results of 20 images for this query.…”

“…Then, the IRP algorithm is used for the feature similarity ranking level fusion and ultimately k top images in the final ranking list are displayed to the user as the retrieval results. This process is Table 2 An example of classification process: the score threshold value has been set to 30 and so the search space has been reduced to eight classes (m′= 5,7,8,9,11,19,21,38) Fig. 12, only two irrelevant images have been retrieved in the first RF (ranks 11 and 14), and in the second iteration of RF, there is no any irrelevant images in the retrieval results (Fig.…”

“…Hence, the shortcomings of this issue and consequently obtaining features with less precision for some images will be compensated by using data fusion techniques and RF. Therefore, we use simple image processing techniques such as adaptive local histogram [8], edge detection filters, adaptive thresholding method [35], and morphological operators to extract the boundary.…”

“…Hence, by filtering out the images of irrelevant classes and reducing the search space, the performance of CBIR systems is improved significantly [7][8][9][10]. Pourghassem and Ghassemian [7] proposed a hierarchical medical image classification method using a merging scheme and multilayer perceptron classifiers.…”

“…In this system, semantic classes are created from overlapping classes in the database. Fesharaki and Pourghassem [8] proposed a classification based on Bayesian rule. Also, Rahman et al [9] proposed a medical image retrieval system with probabilistic multiclass support vector machine (SVM) classifiers and adaptive similarity fusion approach based on a linear combination of individual feature level similarities.…”