Akinobu Shimizu

Segmentation of multiple organs in non-contrast 3D abdominal CT images

ObjectiveWe propose a simultaneous extraction method for 12 organs from non-contrast three-dimensional abdominal CT images.Materials and methodsThe proposed method uses an abdominal cavity standardization process and atlas guided segmentation incorporating parameter estimation with the EM algorithm to deal with the large fluctuations in the feature distribution parameters between subjects. Segmentation is then performed using multiple level sets, which minimize the energy function that considers the hierarchy and exclusiveness between organs as well as uniformity of grey values in organs. To assess the performance of the proposed method, ten non-contrast 3D CT volumes were used.ResultsThe accuracy of the feature distribution parameter estimation was slightly improved using the proposed EM method, resulting in better performance of the segmentation process. Nine organs out of twelve were statistically improved compared with the results without the proposed parameter estimation process. The proposed multiple level sets also boosted the performance of the segmentation by 7.2 points on average compared with the atlas guided segmentation. Nine out of twelve organs were confirmed to be statistically improved compared with the atlas guided method.ConclusionThe proposed method was statistically proved to have better performance in the segmentation of 3D CT volumes.

Medical image analysis of 3D CT images based on extension of Haralick texture features

PURPOSE A new approach to the segmentation of 3D CT images is proposed in an attempt to provide texture-based segmentation of organs or disease diagnosis. 3D extension of Haralick texture features was studied calculating co-occurrences of all voxels in a small cubic region around the voxel. RESULTS For verification, the proposed method was tested on a set of abdominal 3D volumes of patients. Statistically, the improvement in segmentation was significant for most of the organs considered herein. CONCLUSIONS The proposed method has potential application in medical image segmentation, including diagnosis of diseases.

Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

PurposeWe propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation.MethodsThe algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patient- specific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results.ResultsThe effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%.ConclusionsThis study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.

Robust face detection using Gabor filter features

In this paper, we present a classification-based face detection method using Gabor filter features. Taking advantage of the desirable characteristics of spatial locality and orientation selectivity of Gabor filters, we design four filters corresponding to four orientations for extracting facial features from local images in sliding windows. The feature vector based on Gabor filters is used as the input of the face/non-face classifier, which is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). The effectiveness of the proposed method is demonstrated by experiments on a large number of images. We show that using both of the magnitude and phase of Gabor filter response as features, the detection performance is better than that using magnitude only, and using the real part only also performs fairly well. Our detection performance is competitive with those reported in the literature.

Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection

This paper proposes a novel algorithm to estimate a log-compressed K distribution parameter and presents an algorithm to discriminate breast tumors in ultrasonic images. We computed a total of 208 features for discrimination, including those based on a parameter of a log-compressed K-distribution, which quantifies the homogeneity of the echo pattern in the tumor, but is influenced by compression parameters in the ultrasonic device. The proposed algorithm estimates the parameter of the log-compressed K-distribution in a manner free from this influence. To quantify irregularities in tumor shape, pattern-spectrum-based features were newly developed in this paper. The discrimination process uses an ensemble classifier trained by a multiclass AdaBoost learning algorithm (AdaBoost.M2), combined with a sequential feature-selection process. A 10-fold cross-validation test validated the performance, and the results were compared with those of a Mahalanobis distance-based classifier and a multiclass support vector machine. A total of 200 carcinomas, 50 fibroadenomas, and 50 cysts were used in the experiments. This paper demonstrates that the combination of a classifier trained by AdaBoost.M2 and features based on the estimated parameter of a log-compressed K-distribution, as well as those of the pattern spectrum, are useful for the discrimination of tumors.

Face detection from cluttered images using a polynomial neural network

Abstract Automatic detection of human faces from cluttered images is important for face recognition and security applications. This problem is challenging due to the multitude of variations and the confusion between face and background regions. This paper proposes a new face detection method using a polynomial neural network (PNN). To locate the human faces in an image, the local regions in multiscale sliding windows are classified by the PNN to two classes, namely, face and non-face. The PNN takes as inputs the binomials of the projection of the local image onto a feature subspace learned by principal component analysis (PCA). We investigated the influence of PCA on either the face samples or the pooled face and non-face samples. In addition, we integrate the distance from the feature subspace into the PNN to improve the detection performance. In experiments on images with complex backgrounds, the proposed method has produced promising results in terms of high detection rate and low false positive rate.

Optimal image feature set for detecting lung nodules on chest X-ray images

The performance of a computer-aided diagnosis system depends on the feature set used in it. This paper shows the results of image feature selection experiments. We evaluated 210 features to look for the optimum feature set. For the purpose, a forward stepwise selection approach was employed. The area under the receiver operating characteristic (ROC) curve was adopted to evaluate the performance of each feature set. Analysis of the optimally selected feature set is given and the experiments using 247 chest x-ray images are also shown.

A conditional statistical shape model with integrated error estimation of the conditions; Application to liver segmentation in non-contrast CT images

This paper presents a novel conditional statistical shape model in which the condition can be relaxed instead of being treated as a hard constraint. The major contribution of this paper is the integration of an error model that estimates the reliability of the observed conditional features and subsequently relaxes the conditional statistical shape model accordingly. A three-step pipeline consisting of (1) conditional feature extraction from a maximum a posteriori estimation, (2) shape prior estimation through the novel level set based conditional statistical shape model with integrated error model and (3) subsequent graph cuts segmentation based on the estimated shape prior is applied to automatic liver segmentation from non-contrast abdominal CT volumes. Comparison with three other state of the art methods shows the superior performance of the proposed algorithm.

Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume

This paper presents a novel graph cut algorithm that can take into account multi-shape constraints with neighbor prior constraints, and reports on a lung segmentation process from a three-dimensional computed tomography (CT) image based on this algorithm. The major contribution of this paper is the proposal of a novel segmentation algorithm that improves lung segmentation for cases in which the lung has a unique shape and pathologies such as pleural effusion by incorporating multiple shapes and prior information on neighbor structures in a graph cut framework. We demonstrate the efficacy of the proposed algorithm by comparing it to conventional one using a synthetic image and clinical thoracic CT volumes.

Gradient feature extraction for classification-based face detection

Face detection from cluttered images is challenging due to the wide variability of face appearances and the complexity of image backgrounds. This paper proposes a classification-based method for locating frontal faces in cluttered images. To improve the detection performance, we extract gradient direction features from local window images as the input of the underlying two-class classifier. The gradient direction representation provides better discrimination ability than the image intensity, and we show that the combination of gradient directionality and intensity outperforms the gradient feature alone. The underlying classifier is a polynomial neural network (PNN) on a reduced feature subspace learned by principal component analysis (PCA). The incorporation of the residual of subspace projection into the PNN was shown to improve the classification performance. The classifier is trained on samples of face and non-face images to discriminate between the two classes. The superior detection performance of the proposed method is justified in experiments on a large number of images.