Area-efficient HEVC core transform using multi-sized and reusable DCT architectures

Multimedia Tools and Applications - This paper presents an area-efficient and multi-sized DCT architecture for HEVC application. We exploit the commonality in the arithmetic units to increase the...     

Extraction of spiculated parts of mammogram tumors to improve accuracy of classification

Spiculated parts of masses are significant features to classify tumors in digital mammography; however, segmentation, which is used to extract the shape and contour of a tumor, eliminates them. To address this problem, the current study proposes a novel algorithm for extraction of the spiculated pixels of a tumor that are of similar intensity along a line. It first applies the sums of the differences between the central pixel and neighboring pixels in different symmetric orthogonal directions. The minimum difference between two symmetric orthogonal directions specifies the similarity of pixels in one direction as denoting a spiculated part of the mass. These parts then are added to the segmented image to enhance the shape of tumor. The features of the tumor are extracted from the final segmented image to allow its classification as benign or malignant. Simulation results showed that the accuracy and the area under the ROC curve of the proposed method for mini-MIAS and DDSM databases were 91.37% and 93.22% and 0.9776 and 0.9752, respectively. This confirms the effectiveness of the proposed algorithm for extraction of the spiculated parts of a malignant tumor with the aim of increasing the classification accuracy.

     

Mass classification of mammograms using fractal dimensions and statistical features

For classification of tumors in mammography, the major features are extracted from the segmented tumor. However, some details of the tumor margin, such as the spiculated parts, are eliminated in the segmentation step. The current study suggests a new approach for extracting the spiculated parts and tumor core. The proposed method segments the tumor by assessing the similarity of the pixels of the tumor core and dissimilarity of the spiculated parts. Then, the spiculated parts and the tumor core are combined to create the final segmentation. Next, the statistical features and fractal dimensions are extracted from the tumor. The fractal dimension is a measure of complexity of the tumor shape that is effective for discriminating between benign and malignant tumors. The simulation results show that the proposed method is more suitable than other methods. The area under the ROC curve and the accuracy of the proposed method on mini-MIAS were 0.9627 and 89.66% and for DDSM were 0.9777 and 93.50%, respectively. The results confirm the efficiency of the proposed method for extracting the mass core and spiculated parts. They also show that use of the fractal dimension increases the accuracy of classification and complements the other shape features.