Rahmita Wirza

A FAST AND ACCURATE METHOD FOR AUTOMATIC CORONARY ARTERIAL TREE EXTRACTION IN ANGIOGRAMS

Coronary arterial tree extraction in angiograms is an essential component of each cardiac image processing system. Once physicians decide to check up coronary arteries from x-ray angiograms, extraction must be done precisely, fast, automatically and including whole arterial tree to help diagnosis or treatment during the cardiac surgical operation. This application is very helpful for the surgeon on deciding the target vessels prior to coronary artery bypass graft surgery. Some techniques and algorithms are proposed for extracting coronary arteries in angiograms. However, most of them suffer from some disadvantages such as time complexity, low accuracy, extracting only parts of main arteries instead of the full coronary arterial tree, need manual segmentation, appearance of artifacts and so forth. This study presents a new method for extracting whole coronary arterial tree in angiography images using Starlet wavelet transform. To this end, firstly we remove noise from raw angiograms and then sharpen the coronary arteries. Then coronary arterial tree is extracted by applying a modified Starlet wavelet transform and afterwards the residual noises and artifacts are cleaned. For evaluation, we measure proposed method performance on our created data set from 4932 Left Coronary Artery (LCA) and Right Coronary Artery (RCA) angiograms and compared with some state-of-the-art approaches. The proposed method shows much higher accuracy 96% for LCA and 97% for RCA, higher sensitivity 86% for LCA and 89% for RCA, higher specificity 98% for LCA and 99% for RCA and also higher precision 87% for LCA and 93% for RCA angiograms.

Echocardiography Image Segmentation: A Survey

Segmentation is an important step in medical imaging to acquire qualitative measurements such as the location of the desired objects and also for quantitative measurements such as area, volume or the analysis of dynamic behaviour of anatomical structures over time. Among these images, ultrasound images play a crucial role, because they can be produced on video-rate and therefore allows a dynamic analysis of moving structures. In addition, the acquisition of these images is non-invasive, cheap, and does not require ionizing radiations compared to other medical imaging techniques. On the other hand, the automatic segmentation of anatomical structures in ultrasound imagery is a real challenge due to acoustic interferences (speckle noise) and artifacts which are inherent in these images. This paper surveys the literature often recent researches on echocardiography image segmentation methods, focusing on techniques developed for medical. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten recent papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the echocardiography segmentation problem. The contribution of the paper is in three ways: 1) to serve as a tutorial on the field for both clinicians and technologists, 2) to provide an extensive account of segmentation techniques in a comprehensive and systematic manner, and 3) to critically review recent approaches in terms of their performance and degree of clinical evaluation with respect to the final goal of cardiac functional analysis.

Coronary Artery Segmentation in Angiograms with Pattern Recognition Techniques -- A Survey

Medical image processing is nowadays one of the best tools to make an informative model from a raw image of each part of the body, and segmentation is the most important step in which used to extract significant features. Coronary artery segmentation algorithm in angiograms is a fundamental component of each cardiac image processing system. There are lots of techniques and algorithms proposed for extracting coronary arteries in angiograms. But based on our knowledge, there is not any review paper to categorize and compare them together. In this paper, we have divided these algorithms into five major classes and propose a survey for the main class, pattern recognition, which is a famous technique in this manner. We studied all the papers in the pattern recognition class and defined six categories for them: (1) Multi scale approaches (2) Region growing approaches (3) Matching filters approaches (4) Mathematical morphology approaches (5) Skeleton based approaches and (6) Ridge based approaches. Finally, we made a table to compare all the algorithms in each category against criteria such as: user interaction, angiography types, dimensionality, enhancement method, full coronary artery output, whole tree output, and 3D reconstruction ability.

Hybrid of HMM and Fuzzy Logic for handwritten character recognition

This paper presents a hybrid approach of HMM and Fuzzy Logic in the field of handwritten character recognition. Fuzzy Logic is used in the recognition phase while HMM is used in the process of extracting features for the preparation of linguistic variables of the fuzzy rules. Experimental results from a few sample images give a reasonable recognition rate on a more challenging database of lower-case handwritten characters. This proved the proposed hybrid of the two techniques are compatible and can be used to complement each other effectively.

Inspection Strategies for Complex Curved Surfaces Using CMM

The accurate measurement of complex surfaces is difficult. Accuracy demands precision in measuring technology, i.e., the measuring machine and also precise mathematical representation of complex geometries. This paper introduces a method of measuring a complex surface by using a Coordinate Measuring Machine and representing the measured surfaces mathematically. This enables comparison with other surfaces, e.g. the as-designed surface or the original unworn surface. The measurement of the knee prosthesis was taken as a case study.

3D multimodal cardiac data reconstruction using angiography and computerized tomographic angiography registration

BackgroundComputerized tomographic angiography (3D data representing the coronary arteries) and X-ray angiography (2D X-ray image sequences providing information about coronary arteries and their stenosis) are standard and popular assessment tools utilized for medical diagnosis of coronary artery diseases. At present, the results of both modalities are individually analyzed by specialists and it is difficult for them to mentally connect the details of these two techniques. The aim of this work is to assist medical diagnosis by providing specialists with the relationship between computerized tomographic angiography and X-ray angiography.MethodsIn this study, coronary arteries from two modalities are registered in order to create a 3D reconstruction of the stenosis position. The proposed method starts with coronary artery segmentation and labeling for both modalities. Then, stenosis and relevant labeled artery in X-ray angiography image are marked by a specialist. Proper control points for the marked artery in both modalities are automatically detected and normalized. Then, a geometrical transformation function is computed using these control points. Finally, this function is utilized to register the marked artery from the X-ray angiography image on the computerized tomographic angiography and get the 3D position of the stenosis lesion.ResultsThe result is a 3D informative model consisting of stenosis and coronary arteries’ information from the X-ray angiography and computerized tomographic angiography modalities. The results of the proposed method for coronary artery segmentation, labeling and 3D reconstruction are evaluated and validated on the dataset containing both modalities.ConclusionsThe advantage of this method is to aid specialists to determine a visual relationship between the correspondent coronary arteries from two modalities and also set up a connection between stenosis points from an X-ray angiography along with their 3D positions on the coronary arteries from computerized tomographic angiography. Moreover, another benefit of this work is that the medical acquisition standards remain unchanged, which means that no calibration in the acquisition devices is required. It can be applied on most computerized tomographic angiography and angiography devices.

Hybrid Pixel-Based Method for Cardiac Ultrasound Fusion Based on Integration of PCA and DWT

Medical image fusion is the procedure of combining several images from one or multiple imaging modalities. In spite of numerous attempts in direction of automation ventricle segmentation and tracking in echocardiography, due to low quality images with missing anatomical details or speckle noises and restricted field of view, this problem is a challenging task. This paper presents a fusion method which particularly intends to increase the segment-ability of echocardiography features such as endocardial and improving the image contrast. In addition, it tries to expand the field of view, decreasing impact of noise and artifacts and enhancing the signal to noise ratio of the echo images. The proposed algorithm weights the image information regarding an integration feature between all the overlapping images, by using a combination of principal component analysis and discrete wavelet transform. For evaluation, a comparison has been done between results of some well-known techniques and the proposed method. Also, different metrics are implemented to evaluate the performance of proposed algorithm. It has been concluded that the presented pixel-based method based on the integration of PCA and DWT has the best result for the segment-ability of cardiac ultrasound images and better performance in all metrics.

A Review of Ultrasound and Computed Tomography Registration Approaches

Ultrasound is widely used in minimally invasive cardiac procedures due to its convenience of use and noninvasive nature. However, the low quality of ultrasound images often limits their utility as a means for guiding procedures, since it is often difficult to relate the images to their anatomical context. To improve the interpretability of the ultrasound images while maintaining ultrasound as a flexible anatomical and functional real time imaging modality, there is need for some registration techniques that integrate them with their correspond context in high quality pre-operative models such as magnetic resonance imaging or computed tomography images. It is a challenging and remarkable step as through registration, the combined information from multi-modal image acquisition systems such as ultrasound and computed tomography can be obtained by the medical practitioner for better physiological understanding, effective image guidance surgery, treatment, monitoring and diagnostic purposes. An overview of ultrasound and computed tomography registration techniques is presented in this paper.

3D visualization of simple natural language statement using semantic description

Visualizing natural language description is a process of generating 3D scene from natural language statement. Firstly, we should consider the real world visualization and find out what are the keys of visual information that can be extracted from the sentences which represents the most fundamental concepts in both virtual and real environments. This paper focuses on method of generating the 3D scene visualization using semantic simplification description based on logical representation. Based on semantic simplifications description, a concept of parent-child objects relationship is derived. This concept is used as the foundation in determining spatial relationships between objects. Aim of this study is to analyze and match the visual expression and the key information using visual semantic simplification description which presented in logical form. Based on the experimental result, it shows that 60% of the phrases were able to give appropriate depiction as the meaning of the phrase.

A new human heart vessel identification, segmentation and 3D reconstruction mechanism.

BackgroundThe identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.MethodsThis article introduces a new mechanism for identifying the position of vessels leading to the performance of surgery by enhancement of the input image. In addition, develop a 3D vessel reconstruction out of a single-view of a real human heart colour image obtained during open-heart surgery.ResultsReduces the time required for locating the vessel region of interest (ROI). The vessel ROI must appear clearly for the surgeons. Furthermore, reduces the time required for training cardiac surgery of the novice surgeons. The 94.42% accuracy rate of the proposed vessel segmentation method using RGB colour space compares to other colour spaces.ConclusionsThe advantage of this mechanism is to help the surgeons to perform surgery in less time, avoid surgical errors, and to reduce surgical effort. Moreover, the proposed technique can reconstruct the 3D vessel model from a single image to facilitate learning of the heart anatomy as well as training of cardiac surgery for the novice surgeons. Furthermore, extensive experiments have been conducted which reveal the superior performance of the proposed mechanism compared to the state of the art methods.