Daisuke Furukawa

A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows

ObjectiveWe present herein a novel algorithm for architectural distortion detection that utilizes the point convergence index with the likelihood of lines (e.g., spiculations) relating to architectural distortion.Materials and methodsValidation was performed using 25 computed radiography (CR) mammograms, each of which has an architectural distortion with radiating spiculations. The proposed method comprises five steps. First, the lines were extracted on mammograms, such as spiculations of architectural distortion as well as lines in the mammary gland. Second, the likelihood of spiculation for each extracted line was calculated. In the third step, point convergence index weighted by this likelihood was evaluated at each pixel to enhance distortion only. Fourth, local maxima of the index were extracted as candidates for the distortion, then classified based on nine features in the last step.ResultsPoint convergence index without the proposed likelihood generated 84.48/image false-positives (FPs) on average. Conversely, the proposed index succeeded in decreasing this number to 12.48/image on average when sensitivity was 100%. After the classification step, number of FPs was reduced to 0.80/image with 80.0% sensitivity.ConclusionCombination of the likelihood of lines with point convergence index is effective in extracting architectural distortion with radiating spiculations.

Vessel segmentation in eye fundus images using ensemble learning and curve fitting

A novel segmentation algorithm for the detection of retinal vessels in funduscopic images is proposed, in which the benefits of both supervised and unsupervised methods are exploited. Ensemble learning based segmentation (ELBS) is employed for the segmentation of large and medium sized vessels, after which a local curve fitting technique is used for the detection of the thin retinal vessels. The general ELBS algorithm is modified to boost performance by the incorporation of specific knowledge of false negative segmentation result areas. Curve fitting is based on a two-hypotheses polynomial regression and is capable of automatically removing outliers from a point cloud. Evaluation on the DRIVE database compared the presented method favorably to previously published algorithms. Sensitivity and specificity were 0.8854 and 0.9363.

A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists

PurposeIn our previous study, we developed a computer-aided diagnosis (CADx) system using imaging findings annotated by radiologists. The system, however, requires radiologists to input many imaging findings. In order to reduce such an interaction of radiologists, we further developed a CADx system using derived imaging findings based on calculated image features, in which the system only requires few user operations. The purpose of this study is to check whether calculated image features (CFT) or derived imaging findings (DFD) can represent information in imaging findings annotated by radiologists (AFD).MethodsWe calculate 2282 image features and derive 39 imaging findings by using information on a nodule position and its type (solid or ground-glass). These image features are categorized into shape features, texture features and imaging findings-specific features. Each imaging finding is derived based on each corresponding classifier using random forest. To check whether CFT or DFD can represent information in AFD, under an assumption that the accuracies of classifiers are the same if information included in input is the same, we constructed classifiers by using various types of information (CTT, DFD and AFD) and compared accuracies on an inferred diagnosis of a nodule. We employ SVM with RBF kernel as classifier to infer a diagnosis name.ResultsAccuracies of classifiers using DFD, CFT, AFD and CFT

Automatic Liver Segmentation Method based on Maximum A Posterior Probability Estimation and Level Set Method

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Image processing apparatus, image processing method, program, and program storing medium

+ AFD were 0.613, 0.577, 0.773 and 0.790, respectively. Concordance rates between DFD and AFD of shape findings, texture findings and surrounding findings were 0.644, 0.871 and 0.768, respectively.ConclusionsThe results suggest that CFT and AFD are similar information and CFT represent only a portion of AFD. Particularly, CFT did not contain shape information in AFD. In order to decrease an interaction of radiologists, a development of a method which overcomes these problems is necessary.