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Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

Fesharaki NJ, Pourghassem H - J Med Signals Sens (2013)

Bottom Line: Ultimately, in the last levels, this procedure is also continued forming all the classes, separately.In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification.The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran.

ABSTRACT
Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

No MeSH data available.


(a) The 3-category classification with the total accuracy rate of 85.21% by K-nearest neighbor (KNN) classifier and (b) the 4-category classification with the total accuracy rate of 81.15% by KNN classifier
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Figure 10: (a) The 3-category classification with the total accuracy rate of 85.21% by K-nearest neighbor (KNN) classifier and (b) the 4-category classification with the total accuracy rate of 81.15% by KNN classifier

Mentions: In the previous stage, by considering 18 different classes, the classification was applied on the optimal feature space. In this stage, the classification results of category merging in the first level of hierarchical classification structure are used. The threshold of accuracy rate measure is set to δ = 60% and then the class with the highest overlap with the considered class is selected to merge. For this purpose, by sorting the values of the miss-classified ratio, each class that has the highest value of the miss-classified ratio with the considered class is merged. The merging scheme continues until the total accuracy level reaches to 85%. in this research, the threshold of 60% for accuracy rate of each class is acceptable to be prevented from merging of a lot of classes and as a result, defining another features for progressing of the classification structure in more levels with more complexity by choosing a higher value and on the other hand, making it hard to reach to total accuracy by choosing a lower amount. According to the error rate of each level and the repeat of that in the other levels, the threshold of 85% for total accuracy level is also desirable. After applying the merging process on the classification results, 4 classes with an accuracy rate of 88.2% are obtained by using the MLP classifier and 17 neurons in the hidden layer [Figure 9]. By applying the merging process, using KNN classifier with kn = 3 and by choosing the class 13 in different category, the less accuracy rate than 85% for the 4-category classification is resulted. The merging measures are obtained for the 3-category classification with the accuracy rate of 85.21%, which is less than the 4-category classification by using MLP classifier. The results of using KNN classifier are shown in Figure 10.


Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

Fesharaki NJ, Pourghassem H - J Med Signals Sens (2013)

(a) The 3-category classification with the total accuracy rate of 85.21% by K-nearest neighbor (KNN) classifier and (b) the 4-category classification with the total accuracy rate of 81.15% by KNN classifier
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3959005&req=5

Figure 10: (a) The 3-category classification with the total accuracy rate of 85.21% by K-nearest neighbor (KNN) classifier and (b) the 4-category classification with the total accuracy rate of 81.15% by KNN classifier
Mentions: In the previous stage, by considering 18 different classes, the classification was applied on the optimal feature space. In this stage, the classification results of category merging in the first level of hierarchical classification structure are used. The threshold of accuracy rate measure is set to δ = 60% and then the class with the highest overlap with the considered class is selected to merge. For this purpose, by sorting the values of the miss-classified ratio, each class that has the highest value of the miss-classified ratio with the considered class is merged. The merging scheme continues until the total accuracy level reaches to 85%. in this research, the threshold of 60% for accuracy rate of each class is acceptable to be prevented from merging of a lot of classes and as a result, defining another features for progressing of the classification structure in more levels with more complexity by choosing a higher value and on the other hand, making it hard to reach to total accuracy by choosing a lower amount. According to the error rate of each level and the repeat of that in the other levels, the threshold of 85% for total accuracy level is also desirable. After applying the merging process on the classification results, 4 classes with an accuracy rate of 88.2% are obtained by using the MLP classifier and 17 neurons in the hidden layer [Figure 9]. By applying the merging process, using KNN classifier with kn = 3 and by choosing the class 13 in different category, the less accuracy rate than 85% for the 4-category classification is resulted. The merging measures are obtained for the 3-category classification with the accuracy rate of 85.21%, which is less than the 4-category classification by using MLP classifier. The results of using KNN classifier are shown in Figure 10.

Bottom Line: Ultimately, in the last levels, this procedure is also continued forming all the classes, separately.In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification.The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran.

ABSTRACT
Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

No MeSH data available.