Differentiation of Malignant and Benign Masses on Mammograms Using Radial Local Ternary Pattern

Abstract. Texture information of breast masses may be useful in differentiating malignant from benign masses on digital mammograms. Our previous mass classification scheme relied on shape and margin features based on manual contours of masses. In this study, we investigated the texture features that were determined in regions automatically selected from square regions of interest (ROIs) including masses. As a preliminary investigation, 149 ROIs including 91 malignant and 58 benign masses were used for evaluation by a leave-one-out cross validation. The local ternary pattern and local variance were determined in sub regions with the high contrast and a core region. Using an artificial neural network, the classification performance of 0.848 in terms of the area under the receiver operating characteristic curve was obtained

Eigenspace Template Matching for Detection of Lacunar Infarcts on MR Images

Abstract Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction. However, accurate identification is often hindered by the difficulty in distinguishing between lacunar infarcts and enlarged Virchow-Robin spaces. Therefore, we developed a computer-aided detection (CAD) scheme for the detection of lacunar infarcts. Although our previous CAD method indicated a sensitivity of 96.8 % with 0.71 false positives (FPs) per slice, further reduction of FPs remained an issue for the clinical application. Thus, the purpose of this study is to improve our CAD scheme by using template matching in the eigenspace. Conventional template matching is useful for the reduction of FPs, but it has the following two pitfalls: (1) It needs to maintain a large number of templates to improve the detection performance, and (2) calculation of the crosscorrelation coefficient with these templates is time consuming. To solve these problems, we used template matching in the lower dimension space made by a principal component analysis. Our database comprised 1,143 T 1 -and T 2 -weighted images obtained from 132 patients. The proposed method was evaluated by using twofold cross-validation. By using this method, 34.1 % of FPs was eliminated compared with our previous method. The final performance indicated that the sensitivity of the detection of lacunar infarcts was 96.8 % with 0.47 FPs per slice. Therefore, the modified CAD scheme could improve FP rate without a significant reduction in the true positive rate

Classifying breast masses in volumetric whole breast ultrasound data: a 2.5-dimensional approach

The aim of this paper is to investigate a 2.5-dimensional approach in classifying masses as benign or malignant in volumetric anisotropic voxel whole breast ultrasound data. In this paper, the term 2.5-dimensional refers to the use of a series of 2-dimensional images. While mammography is very effective in breast cancer screening in general, it is less sensitive in detecting breast cancer in younger women or women with dense breasts. Breast ultrasonography does not have the same limitation and is a valuable adjunct in breast cancer detection. The current study focuses on a new 2.5-dimensional approach in analyzing the volumetric whole breast ultrasound data for mass classification

Usefulness of presentation of similar images in the diagnosis of breast masses on mammograms: comparison of observer performances in Japan and the USA

Abstract Computer-aided diagnosis has potential in improving radiologists' diagnosis, and presentation of similar images as a reference may provide additional useful information for distinction between benign and malignant lesions. In this study, we evaluated the usefulness of presentation of reference images in observer performance studies and compared the results obtained by groups of observers practicing in the United States and Japan. The results showed that the presentation of the reference images was generally effective for both groups, as the areas under the receiver operating characteristic curves improved from 0.915 to 0.924 for the group in the US and from 0.913 to 0.925 for the group in Japan, although the differences were marginally (p = 0.047) and not (p = 0.13) statistically significant, respectively. There was a slight difference between the two groups in the way that the observers reacted to some benign cases, which might be due to differences in the population of screenees and in the socioclinical environment. In the future, it may be worthwhile to investigate the development of a customized system for physicians in different socio-clinical environments

Investigation of similarity measures for selection of similar images in computer -aided diagnosis of breast lesions on mammograms

The interpretation of mammograms can be difficult. Some breast cancers might be misclassified as benign lesions, and many benign lesions are sent for biopsy. The presentation of images with known pathology similar to an unknown case can be useful. The objective of this study was to investigate similarity measures for the selection of similar images in the distinction between benign and malignant lesions on mammograms. The images of masses and clustered microcalcifications were obtained from the Digital Database for Screening Mammography. In order to select similar images that are really similar and useful for radiologists, subjective similarity ratings for 300 pairs of masses and 300 pairs of clustered microcalcifications were obtained from ten breast radiologists, and the average ratings were employed as a "gold standard" for this study. The result indicated that the similarity measures determined based on pixel-value correlation of images and likelihood of malignancy of lesions were not very useful. The similarity measures based on the distances in the selected image feature space provided moderate correlations with the gold standard. The correlations were improved when similarity measures were determined by use of the ANN trained with the image features and subjective similarity ratings. The usefulness of similar images was evaluated in an observer study. Sixty cases were selected as unknown cases, and a set of benign and malignant images similar to each unknown image was selected based on the sizes and similarity measures. Eleven radiologists provided their confidence level regarding the malignancy of the lesions without and with the similar images. The result indicated that the observers' performances without and with similar images were comparable in terms of the area under the curve of the receiver operating characteristic analysis. However, in terms of the change in confidence level of malignancy, there were many cases in which the similar images had a beneficial effect to the observers. The presentation of similar images has a potential to increase radiologists' confidence and improve their diagnostic performance. For similar images to be useful to radiologists, the similar-image database and the selection scheme must be further improved and reevaluated in the future

Automated blood vessel extraction using local features on retinal images

ABSTRACT An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak to turned image, thus a method was improved by the addition of HLAC features to a polar transformed image. The blood vessels were classified using an artificial neural network (ANN) with HLAC features using 105 mask patterns as input. To improve performance, the second ANN (ANN2) was constructed by using the green component of the color retinal image and the four output values of ANN, Gabor filter, double-ring filter and black-top-hat transformation. The retinal images used in this study were obtained from the "Digital Retinal Images for Vessel Extraction" (DRIVE) database. The ANN using HLAC output apparent white values in the blood vessel regions and could also extract blood vessels with low contrast. The outputs were evaluated using the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis. The AUC of ANN2 was 0.960 as a result of our study. The result can be used for the quantitative analysis of the blood vessels