| Summary: | 碩士 === 國立清華大學 === 電機工程學系 === 101 === This research investigates the hand radiogram with knuckles which were processed by the segmentation, feature extraction and bone age assessment. In the segmentation phase, we apply the difference in strength to segment the region of epiphyseal and metaphyseal. For the feature extraction, we extract 10 and 8 features according to the different knuckles. To simplify the analysis, we introduce PCA technique to reduce the dimensionality from 10 features to 8 features, for the improvement of the classification accuracy.
In the beginning, a left hand radiogram with its index finger, middle finger and ring finger, corresponding to the distal, middle and proximal phalanges, were located and extracted. Next, the nine knuckles were estimated individually by bone age assessment to generate nine ages. The nine ages were involved in the comparison with the chronological age for building an automatic bone age assessment.
Since the classification performance of the binary decision tree is better than the multiple decision tree, the binary decision tree was chosen in the following analysis. Subsequently, the tree function of Matlab was involved and modified to be able to produce the same layers for each branch. In our study, a modified binary decision tree was evaluated their difference between the two analyses of each 9 knuckles and the integration of 9 knuckles. The results were shown the lower accuracy for the integration of 9 knuckles, so we decide to analyze the 9 knuckles separately.
The KNN classification was selected to analyze the PCA features for each knuckle. Then, the binary decision tree was set 4 layers and the sum of 16 outcomes was obtained to correspond with the chronological ages ranged from 1 to 16.
The Leave-one-out cross validation is a useful test for the stability and accuracy, and we integrate the Leave-one-out with binary decision tree for assessing the bone age. The radiograms include 160 boys and 160 girls.
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