Multiresolution analysis using wavelet, ridgelet, and curvelet transforms for medical image segmentation

Copyright @ 2011 Shadi AlZubi et al. This article has been made available through the Brunel Open Access Publishing Fund.The experimental study presented in this paper is aimed at the development of an automatic image segmentation system for classifying region of interest (ROI) in medical images which are obtained from different medical scanners such as PET, CT, or MRI. Multiresolution analysis (MRA) using wavelet, ridgelet, and curvelet transforms has been used in the proposed segmentation system. It is particularly a challenging task to classify cancers in human organs in scanners output using shape or gray-level information; organs shape changes throw different slices in medical stack and the gray-level intensity overlap in soft tissues. Curvelet transform is a new extension of wavelet and ridgelet transforms which aims to deal with interesting phenomena occurring along curves. Curvelet transforms has been tested on medical data sets, and results are compared with those obtained from the other transforms. Tests indicate that using curvelet significantly improves the classification of abnormal tissues in the scans and reduce the surrounding noise

Gaussian mixture model based probabilistic modeling of images for medical image segmentation

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin

BRAIN MR IMAGE SEGMENTATION BY MODIFIED ACTIVE CONTOURS AND CONTOURLET TRANSFORM

Multiresolution analysis is often used for image representation and processing because it can represent image at the split resolution and scale space. In this paper, a novel medical image segmentation algorithm is proposed that combines contourlet transform and modified active contour model. This method has a new energy formulation by representing the image with the coefficients of a contourlet transform. This results fast and accurate convergence of the contour towards the object boundary. Medical image segmentation using contourlet transforms has shown significant improvement towards the weak and blurred edges of the Magnetic Resonance Image (MRI). Also, the computational complexity is less compared to using traditional level sets and variational level sets for medical image segmentation. The special property of the contourlet transform is that, the directional information is preserved in each sub-band and is captured by computing its energy. This energy is capable of enhancing weak and complex boundaries in details. Performance of medical image segmentation algorithm using contourlet transform is compared with other deformable models in terms of various performance measures

An Automatic Level Set Based Liver Segmentation from MRI Data Sets

A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results

Medical Image Segmentation Combining Level Set Method and Deep Belief Networks

Medical image segmentation is an important step in medical image analysis, where the main goal is the precise delineation of organs and tumours from medical images. For instance there is evidence in the field that shows a positive correlation between the precision of these segmentations and the accuracy observed in classification systems that use these segmentations as their inputs. Over the last decades, a vast number of medical image segmentation models have been introduced, where these models can be divided into five main groups: 1) image-based approaches, 2) active contour methods, 3) machine learning techniques, 4) atlas-guided segmentation and registration and 5) hybrid models. Image-based approaches use only intensity value or texture for segmenting (i.e., thresholding technique) and they usually do not produce precise segmentation. Active contour methods can use an explicit representation (i.e., snakes) with the goal of minimizing an energy function that forces the contour to move towards strong edges and maintains the contour smoothness. The use of implicit representation in active contour methods (i.e., level set method) embeds the contour as zero level set of a higher dimensional surface (i.e., the curve representing the contour does not need to be parameterized as in the Snakes model). Although successful, the main issue with active contour methods is the fact that the energy function must contain terms describing all possible shape and appearance variations, which is a complicated task given that it is hard to design by hand all these terms. Also, this type of active contour methods may get stuck at image regions that do not belong to the object of interest. Machine learning techniques address this issue by automatically learning shape and appearance models using annotated training images. Nevertheless, in order to meet the high accuracy requirements of medical image analysis applications, machine learning methods usually need large and rich training sets and also face the complexity of the inference process. Atlas-guided segmentation and registration use an atlas image, which is constructed based on manually segmentation images. The new image is segmented by registering it with the atlas image. These techniques have been applied successfully in many applications, but they still face some issues, such as their ability to represent the variability of anatomical structure and scale in medical image, and the complexity of the registration algorithms. In this work, we propose a new hybrid segmentation approach by combining a level set method with a machine learning approach (deep belief network). Our main objective with this approach is to achieve segmentation accuracy results that are either comparable or better than the ones produced with machine learning methods, but using relatively smaller training sets. These weaker requirements on the size of training sets is compensated by the hand designed segmentation terms present in typical level set methods, that are used as prior information on the anatomy to be segmented (e.g., smooth contours, strong edges, etc.). In addition, we choose a machine learning methodology that typically requires smaller annotated training sets, compared to other methods proposed in this field. Specifically, we use deep belief networks, with training sets consisting to a large extent of un-annotated training images. In general, our hybrid segmentation approach uses the result produced by the deep belief network as a prior in the level set evolution. We validate this method on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 left ventricle segmentation challenge database and on the Japanese Society of Radiological Technology (JSRT) lung segmentation dataset. The experiments show that our approach produces competitive results in the field in terms of segmentation accuracy. More specifically, we show that the use of our proposed methodology in a semi-automated segmentation system (i.e., using a manual initialization) produces the best result in the field in both databases above, and in the case of a fully automated system, our method shows results competitive with the current state of the art.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaIn recent years, medical visualization has evolved from simple 2D images on a light board to 3D computarized images. This move enabled doctors to find better ways of planning surgery and to diagnose patients. Although there is a great variety of 3D medical imaging software, it falls short when dealing with anesthesiology acts. Very little anaesthesia related work has been done. As a consequence, doctors and medical students have had little support to study the subject of anesthesia in the human body. We all are aware of how costly can be setting medical experiments, covering not just medical aspects but ethical and financial ones as well. With this work we hope to contribute for having better medical visualization tools in the area of anesthesiology. Doctors and in particular medical students should study anesthesiology acts more efficiently. They should be able to identify better locations to administrate the anesthesia, to study how long does it take for the anesthesia to affect patients, to relate the effect on patients with quantity of anaesthesia provided, etc. In this work, we present a medical visualization prototype with three main functionalities: image pre-processing, segmentation and rendering. The image pre-processing is mainly used to remove noise from images, which were obtained via imaging scanners. In the segmentation stage it is possible to identify relevant anatomical structures using proper segmentation algorithms. As a proof of concept, we focus our attention in the lumbosacral region of the human body, with data acquired via MRI scanners. The segmentation we provide relies mostly in two algorithms: region growing and level sets. The outcome of the segmentation implies the creation of a 3D model of the anatomical structure under analysis. As for the rendering, the 3D models are visualized using the marching cubes algorithm. The software we have developed also supports time-dependent data. Hence, we could represent the anesthesia flowing in the human body. Unfortunately, we were not able to obtain such type of data for testing. But we have used human lung data to validate this functionality