Comparison between Automatic and Semiautomatic Thresholding Method for Mammographic Density Classification

Abstract

Mammographic density is a novel independent risk factor of breast cancer that reflects the amount of fibroglandular tissue. Breast Imaging Reporting and Data System (BIRADS) density is one of the mammographic density classification schemes which are most widely used by radiologists. Initially, the method used for assessing mammographic density was subjective and qualitative. Recently however, the measurement of mammographic density is more objective and quantitative. In this paper, we propose an alternative model of breast cancer risk factor assessment based on a quantitative approach of density mammogram. This model consists of pre-processing, breast area counting, fibroglandular tissue area counting that uses maximum entropy and multilevel thresholds, and finally breast density counting to determine the risk factor of breast cancer. The proposed model has been tested on a private database from Oncology Clinic Kotabaru, Yogyakarta, Indonesia consisting of 30 mammograms and has been analyzed by some radiologists using the semiautomatic threshold. The result shows that percentage of mammographic density counted by maximum entropy threshold method has the accuracy, sensitivity and specificity of about 87%, 73% and 91% respectively compared to the semiautomatic thresholding method. On the other hand, the accuracy, sensitivity and specificity resulted from using multilevel threshold is about 93%, 87% and 96% respectively. The obtained results suggest that multilevel threshold is perfectly suited for getting quantitative measurement of mammographic density as one of the strongest risk factors for breast cancer.

Comparison of Histogram Features and Co-occurrence Matrix in Identification of Lung Cancer X-Ray Images with Naive-Bayes Method

“…Histogram features are obtained directly from the digital image matrix, which states the gray level value of each pixel in the image [3,5]. The co-occurrence matrix features are obtained from the co-occurrence matrix, which is states the probability value of the adjacency relationship between two pixels at a certain distance and orientation angle [17].…”

“…The subsequent development is the application of co-occurrence matrix features for detecting and identifying objects in the image [17]. Features Research by Noor et al [18] shows that co-occurrence matrix or GLCM features are pretty well applied to X-ray images.…”

A Comparative Study of Thresholding Algorithms on Breast Area and Fibroglandular Tissue

“…The complete results of the threshold value obtained for each mammogram with the five thresholding algorithms are shown in Table 4. Subsequently, the computation results of the performance of the five thresholding algorithms are shown in Table (3) and (4). A computation of the mean value was done and the results are shown in Table (5). The smaller the value indicates the smaller the difference, meaning that the images resulted from the segmentation by using thresholding algorithm is close to the images resulted from segmentation by using semi-automated thresholding by Radiologists.…”

“…The automated thresholding methods that have been used to obtain fibroglandular tissue include: Gaussian mixture modeling by [8] and minimum-cross entropy by [9], while the automatic thresholding methods that have been used to get breast areas include: row by row method thresholding (RRT) and average row threshold (ART) by [10] and by employing the threshold value of 18 by [11]. [4] had proposed a calculation model of breast cancer risks by computing the percentage of mammographic density. This model could be applied as a reference to help decrease breast cancer risks.…”

“…Meanwhile, the minimum cross entropy by [9] is used to obtain the fibroglandular tissue area by separating the fat tissue from the breast area. [4] has developed the computational model in determining the breast cancer risk factors based on the percentage of mammographic density. The use of Zack algorithm to obtain the breast area and multilevel thresholding to obtain fibroglandular tissue area in the proposed model has better accuracy, sensitivity and specificity if compared to the use of maximum Zack algorithm and maximum entropy.…”