Yeong Kyeong Seong

Fourier-based shape feature extraction technique for computer-aided B-Mode ultrasound diagnosis of breast tumor

Early detection of breast tumor is critical in determining the best possible treatment approach. Due to its superiority compared with mammography in its possibility to detect lesions in dense breast tissue, ultrasound imaging has become an important modality in breast tumor detection and classification. This paper discusses the novel Fourier-based shape feature extraction techniques that provide enhanced classification accuracy for breast tumor in the computer-aided B-mode ultrasound diagnosis system. To demonstrate the effectiveness of the proposed method, experiments were performed using 4,107 ultrasound images with 2,508 malignancy cases. Experimental results show that the breast tumor classification accuracy of the proposed technique was 15.8%, 5.43%, 17.32%, and 13.86% higher than the previous shape features such as number of protuberances, number of depressions, lobulation index, and dissimilarity, respectively.

Multiobjective evolutionary optimization for tumor segmentation of breast ultrasound images

This paper proposes a robust multiobjective evolutionary algorithm (MOEA) to optimize parameters of tumor segmentation for ultrasound breast images. The proposed algorithm employs efficient schemes for reinforcing proximity to Pareto-optimal and diversity of solutions. They are designed to solve multiobjective problems for segmentation accuracy and speed. First objective is evaluated by difference between the segmented outline and ground truth. Second objective is evaluated by elapsed time during segmentation process. The experimental results show the effectiveness of the proposed algorithm compared with conventional MOEA from the viewpoint of proximity to the Pareto-optimal front (improved by 16.4% and 12.4%). Moreover, segmentation results of proposed algorithm describe faster segmentation speed (1.97 second) and higher accuracy (8% Jaccard).

Ultrasound breast lesion segmentation using adaptive parameters

In computer aided diagnosis for ultrasound images, breast lesion segmentation is an important but intractable procedure. Although active contour models with level set energy function have been proposed for breast ul- trasound lesion segmentation, those models usually select and x the weight values for each component of the level set energy function empirically. The xed weights might a ect the segmentation performance since the characteristics and patterns of tissue and tumor di er between patients. Besides, there is observer variability in probe handling and ultrasound machine gain setting. Hence, we propose an active contour model with adaptive parameters in breast ultrasound lesion segmentation to overcome the variability of tissue and tumor patterns between patients. The main idea is to estimate the optimal parameter set automatically for di erent input images. We used regression models using 27 numerical features from the input image and an initial seed box. Our method showed better results in segmentation performance than the original model with xed parameters. In addition, it could facilitate the higher classi cation performance with the segmentation results. In conclusion, the proposed active contour segmentation model with adaptive parameters has the potential to deal with various di erent patterns of tissue and tumor e ectively.

Non-rigid ultrasound image registration using generalized relaxation labeling process

This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image’s local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.

Fast microcalcification detection in ultrasound images using image enhancement and threshold adjacency statistics

The existence of microcalcifications (MCs) is an important marker of malignancy in breast cancer. In spite of the benefits in mass detection for dense breasts, ultrasonography is believed that it might not reliably detect MCs. For computer aided diagnosis systems, however, accurate detection of MCs has the possibility of improving the performance in both Breast Imaging-Reporting and Data System (BI-RADS) lexicon description for calcifications and malignancy classification. We propose a new efficient and effective method for MC detection using image enhancement and threshold adjacency statistics (TAS). The main idea of TAS is to threshold an image and to count the number of white pixels with a given number of adjacent white pixels. Our contribution is to adopt TAS features and apply image enhancement to facilitate MC detection in ultrasound images. We employed fuzzy logic, tophat filter, and texture filter to enhance images for MCs. Using a total of 591 images, the classification accuracy of the proposed method in MC detection showed 82.75%, which is comparable to that of Haralick texture features (81.38%). When combined, the performance was as high as 85.11%. In addition, our method also showed the ability in mass classification when combined with existing features. In conclusion, the proposed method exploiting image enhancement and TAS features has the potential to deal with MC detection in ultrasound images efficiently and extend to the real-time localization and visualization of MCs.

Computer-aided lesion diagnosis in B-mode ultrasound by border irregularity and multiple sonographic features

In this paper, we propose novel feature extraction techniques which can provide a high accuracy rate of mass classification in the computer-aided lesion diagnosis of breast tumor. Totally 290 features were extracted using the newly developed border irregularity feature extractor as well as multiple sonographic features based on the breast imaging-reporting and data system (BI-RADS) lexicons. To demonstrate the performance of the proposed features, 4,107 ultrasound images containing 2,508 malignant cases were used. The clinical results demonstrate that the proposed feature combination can be an integral part of ultrasound CAD systems to help accurately distinguish benign from malignant tumors.

Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine

In this study, we propose a computer-aided classification scheme of liver tumor in 3D ultrasound by using a combination of deformable model segmentation and support vector machine. For segmentation of tumors in 3D ultrasound images, a novel segmentation model was used which combined edge, region, and contour smoothness energies. Then four features were extracted from the segmented tumor including tumor edge, roundness, contrast, and internal texture. We used a support vector machine for the classification of features. The performance of the developed method was evaluated with a dataset of 79 cases including 20 cysts, 20 hemangiomas, and 39 hepatocellular carcinomas, as determined by the radiologists visual scoring. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 89.8% of cases, and achieved 93.7% accuracy in classification of cyst and hemangioma.

Tumor Subtype-Specific Parameter Optimization in a Hybrid Active Surface Model for Hepatic Tumor Segmentation of 3D Liver Ultrasonograms

Segmentation of hepatic tumors is a clinically demanding task for improving reliability in diagnosis and treatment procedures, and yet remains a challenging problem due to their highly noisy, low contrast, and blurry imaging nature. However, once correctly segmented, the shape and volume information of a tumor may provide useful information for radiological decision making. In this study, we propose an active surface model. The model combines edge, region, and contour smoothness energies. We extracted qualitative appearance features from three hepatic tumor subtypes and use them to adjust the weights of the energy terms in order to determine an optimized set of parameters for each tumor subtype. The performance of the developed method was evaluated with a dataset of 60 cases including 18 hepatic simple cysts, 18 hemangiomas, and 24 hepatocellular carcinomas, as determined by the radiologists visual assessment. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 87% of cases.

Automatic tumor detection in the constrained region for ultrasound breast CAD

In this paper we propose a new method to segment a breast image into several regions. Tumor detection region is constrained to the region only in glandular tissue because the tumors usually occur at glandular tissue in the breast anatomy. We extract texture feature for each point and classify them as several layers using a random forest classifier. Classified points are merged into a large region and small regions are removed by postprocessing. The accuracy of glandular tissue detection rate was about 90%. We applied the conventional tumor detection method in this segmented glandular tissue. After several tests we obtained that tumor detection accuracy improved for 14% and detection time was also reduced. With this method, we can achieve the improvement both on tumor detection accuracy and on the processing time.