Seong-Jae Lim

Automatic liver segmentation for volume measurement in CT Images

Computed tomography (CT) images have been widely used for diagnosis of liver disease and volume measurement for liver surgery or transplantation. Automatic liver segmentation and volume measurement based on the segmentation are the most essential parts in computer-aided diagnosis for liver CT as well as computer-aided surgery. However, liver segmentation, in general, has been performed by outlining the medical image manually or segmenting CT images semi-automatically because surface features of the liver and partial-volume effects make automatic discrimination from other adjacent organs or tissues very difficult. Accordingly, in this paper, we propose a new approach to automatic segmentation of the liver for volume measurement in sequential CT images. Our method analyzes the intensity distribution of several abdominal CT samples and exploits a priori knowledge, such as CT numbers and location of the liver to identify coherent regions that correspond to the liver. The proposed scheme utilizes recursively morphological filter with region-labeling and clustering to detect the search range and to generate the initial liver contour. In this search range, we deform liver contour using the labeling-based search algorithm following pattern features of the liver contour. Lastly, volume measurement is automatically performed on the segmented liver regions. The experimental measurement of area and volume is compared with those using manual tracing method as a gold standard by the radiological doctors, and demonstrates that this algorithm is effective for automatic segmentation and volume measurement method of the liver.

Automatic segmentation of the liver in CT images using the watershed algorithm based on morphological filtering

Liver segmentation is one of the most basic and important parts in computer-aided diagnosis for liver CT. Although various segmentation methods have been proposed for medical imaging, most of them generally do not perform well in segmenting the liver from CT images because of surface features of the liver and difficulty of discrimination from other adjacent organs. In this paper, we propose a new scheme for automatic segmentation of the liver in CT images. The pro-posed scheme is carried out on region-of-interest (ROI) blocks that include regions of the liver with high probabilities. The ROI approach saves unnecessary computational loss in finding the accurate boundary of the liver. The proposed method utilizes the composition of morphological filters with a priori knowledge, such as the general location or the approximate intensity of the liver to detect the initial boundary of the liver. Then, we make the gradient image with the weight of an initial liver boundary and segment the liver region by using an immersion-based watershed algorithm in the gradient image. Finally, a refining process is carried out to acquire a more accurate liver region.

Segmentation of the liver using the deformable contour method on CT images

Automatic liver segmentation from abdominal computed tomography (CT) images is one of the most important steps for computer-aided diagnosis (CAD) for liver CT. However, the liver must be separated manually or semi-automatically since surface features of the liver and partial-volume effects make automatic discrimination from other adjacent organs or tissues very difficult. In this paper, we present an unsupervised liver segmentation algorithm with three steps. In the preprocessing, we simplify the input CT image by estimating the liver position using a prior knowledge about the location of the liver and by performing multilevel threshold on the estimated liver position. The proposed scheme utilizes the multiscale morphological filter recursively with region-labeling and clustering to detect the search range for deformable contouring. Most of the liver contours are positioned within the search range. In order to perform an accurate segmentation, we produce the gradient-label map, which represents the gradient magnitude in the search range. The proposed algorithm performed deformable contouring on the gradient-label map by using regular patterns of the liver boundary. Experimental results are comparable to those of manual tracing by radiological doctors and shown to be efficient.

3-D Active Shape Image Segmentation Using a Scale Model

In this paper, we propose an active shape image segmentation method for three-dimensional (3-D) medical images. The approach consists of a generation method of a 3-D shape model and a segmentation method using a scale model. The 3-D shape generation method uses a tetrahedron algorithm for landmarking based on the view of geometry. After generating the 3-D model, we extend the shape model training and gray-level model training of two-dimensional (2-D) active shape models (ASMs). We use the integrated modeling process with scale and gray-level models for the appearance profile to represent local structure. Scale models are more compact, have more information in the region close to boundary, and have less information in the region far away from the boundary. Therefore, a scale can be considered as a weighting factor. Experimental results are comparable to those of manual tracing by the radiologist and 2-D ASMs, and demonstrate that this algorithm is effective for the semi-automatic segmentation method of livers

A new general method of 3D model generation for active shape image segmentation

For 3D model-based approaches, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently remains an open problem. In this paper, we propose a novel, general method for the generation of 3D statistical shape models. Given a set of training 3D shapes, 3D model generation is achieved by 1) building the mean model from the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean model, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. Previous 3D modeling efforts all had severe limitations in terms of the object shape, geometry, and topology. The proposed method is very general without such assumptions and is applicable to any data set.

Automatic generation system of the 3D character model for Smart TV contents production

This paper presents a fully-automatic 3D character model generating system for Smart TV contents. The system applies 3D template model transferring and sweep animation technique for automatic rigging of the 3D character model.

Lossless Data Hiding for Medical Images with Patient Information

This paper presents a lossless data hiding algorithm for medical images, where we embed the patient information into the segmented liver region of the CT image. This algorithm utilizes the characteristics of difference images and modifies pixel values slightly to embed a large amount of data while keeping high visual quality.

A New Quantization for Rate Control with Frame Variation Consideration

The main role of rate control in video coding is to handle a trade-off between a bandwidth limit and a video quality. In the low-delay video communication systems, its responsibility is more important. In this work, we propose a new rate control algorithm. In this paper, we have two approaches: one is a consideration of variations among the frames at the frame-level bit allocation, and the other is a model-based adaptive quantization at the macroblock-level. The proposed scheme is done with a low computational complexity. For simulations, we implement the proposed algorithm to H.263 video codec since it is widely used as a video coding tool in real-time mobile AV systems. The proposed scheme shows more improved picture quality than H.263 TMN8 and generates coding bits close to the target bits. The results also show more regular PSNR fluctuation than H.263 TMN8