Sarada Prasad Dakua

LV Contour Extraction from Cardiac MR Images Using Random Walks Approach

Heart failures are of increasing importance due to increasing life expectation. For clinical diagnosis parameters for the condition of hearts are needed and can be derived automatically by image processing. Accurate and fast image segmentation algorithms are of paramount importance for a wide range of medical imaging applications. In this paper, we present a method using heat equation with variable threshold technique towards seeds selection in random walk based image segmentation.

Automatic Left Ventricular Contour Extraction from Cardiac Magnetic Resonance Images Using Cantilever Beam and Random Walk Approach

Heart failure is a well-known debilitating disease. From clinical point of view, segmentation of left ventricle (LV) is important in a cardiac magnetic resonance (CMR) image. Accurate parameters are desired for better diagnosis. Proper and fast image segmentation of LV is of paramount importance prior to estimation of these parameters. We prefer random walk approach over other existing techniques due to two of its advantages: (1) robustness to noise and, (2) it does not require any special condition to work. Performance of the method solely depends on the selection of initial seed and parameter β. Problems arise while applying this method to different kind of CMR images bearing different ischemia. It is due due to their implicit geometry definitions unlike general images, where the boundary of LV in the image is not available in an explicit form. This type of images bear multi-labeled LV and the manual seed selection in these images introduces variability in the results. In view of this, the paper presents two modifications in the algorithm: (1) automatic seed selection and, (2) automatic estimation of β from the image. The highlight of our method is its ability to succeed with minimum number of initial seeds.

Patient oriented graph-based image segmentation

Abstract Despite increased image quality including medical imaging, image segmentation continues to represent a major bottleneck in practical applications due to noise and lack of contrast. In this paper, we present a new methodology to segment noisy, low contrast medical images, with a view to developing practical applications. Firstly, the contrast of the image is enhanced and then a modified graph-based method is followed. This paper has mainly two contributions: (1) a contrast enhancement stage performed by suitably utilizing the noise present in the medical data. This step is achieved through stochastic resonance theory applied in the wavelet domain and (2) a new weighting function is proposed for traditional graph-based approaches. Both qualitative (by our clinicians/radiologists) and quantitative evaluation performed on publicly available computed tomography (CT) (MICCAI 2007 Grand Challenge workshop database) and cardiac magnetic resonance (CMR) databases reflect the potential of the proposed method even in the presence of tumors/papillary muscles.

Use of chaos concept in medical image segmentation

Despite its long track record, segmentation in medical image computing still remains an active field of research, largely due to the complexities of in-vivo anatomical structures, cross-subject and cross-modality variations. Clinically, it has many benefits for effective patient management, both in terms of pre-operative planning and post-operative assessment of the efficacy of therapeutic procedures. Research efforts are focused on novel, clinician friendly, robust and fast segmentation methodologies. In this paper, we present a novel algorithm for efficient segmentation based on Chaotic theory; the preliminary results show the potential of the proposed technique.

Weighting function in Random Walk based left ventricle segmentation

Cardiac Magnetic Resonance (CMR) image segmentation is a crucial step before physicians go for patient diagnoses, related image guided surgery or medical data visualization. Most of the existing algorithms are effective under certain circumstances. On the other hand, Random Walk approach is robust for image segmentation in every condition. Weighting function plays an important role for a successful segmentation in the approach. In this work, an attempt has been made to study the behavior of the weighting function with respect to the intensity distribution in the object to be segmented. In this work, we present a weighting function viz. derivative of Gaussian, that is proved to yield better segmentation results while applying on ischemic CMR images, where objects are obscure. Virtuous results on CMR images describes the potential of the weighting function.

Detection of Left Ventricular Myocardial Contours from Ischemic Cardiac MR Images

Abstract Quantitative evaluation of cardiac function from cardiac magnetic resonance (CMR) images requires the identification of the myocardial walls. This generally requires the clinician to view the image and interactively trace the contours. The myocardial wall of the left ventricle in CMR images, obtained from subjects having serious diseases, is obscure, henceforth, its detection is a tough task. In this paper, an approach to outlining the left ventricular contour is proposed. The utilization of a random walk approach is shown in order to extract the blood pool boundary or endocardium (the inner side of the left ventricular myocardial wall). Inaccurate segmentation is resulted, while applying the approach to ischemic CMR images, because this type of images bear multilabeled blood pool and the manual seed(s) selection in these images introduces variability in the results. In view of this, the paper presents two modifications in the algorithm: (1) automatic seed(s) selection and (2) introduction of Laplacian of difference of Gaussian weighting function. Subsequently, a modified version of an active contour method is implemented to extract the epicardium (the outer side of the left ventricular myocardial wall). This outer contour is achieved by taking the blood pool boundary as its initial contour. Basically, this method is based on active contour without edges. Promising experimental results in CMR images demonstrate the potentials of our approach.

A Strategic Approach for Cardiac MR Left Ventricle Segmentation

Quantitative evaluation of cardiac function from cardiac magnetic resonance (CMR) images requires the identification of the myocardial walls. This generally requires the clinician to view the image and interactively trace the contours. Especially, detection of myocardial walls of left ventricle is a difficult task in CMR images that are obtained from subjects having serious diseases. An approach to automated outlining the left ventricular contour is proposed. In order to segment the left ventricle, in this paper, a combination of two approaches is suggested. Difference of Gaussian weighting function (DoG) is newly introduced in random walk approach for blood pool (inner contour) extraction. The myocardial wall (outer contour) is segmented out by a modified active contour method that takes blood pool boundary as the initial contour. Promising experimental results in CMR images demonstrate the potentials of our approach.

Effect of β in random walk approach for LV contour extraction

In todays world, increasing life expectation have made the heart failures of important concern. For clinical diagnosis, parameters for the condition of heart are needed. Accurate and fast image segmentation algorithms are of paramount importance prior to the calculation of these parameters. An automatic method for segmenting the cardiac magnetic resonance (CMR) images is always desired to increase the accuracy. We prefer random walk method due to its noise robustness and unconditional approach over other segmentation algorithms. Performance of the method solely depends on the selection of the free parameter β, which uses to be decided manually. The accuracy of the output significantly depends on this parameter. In this work, we present a method to decide its value automatically enhancing the accuracy of the performance.

LV Contour Extraction Using Difference of Gaussian Weighting Function and Random Walk Approach

Image segmentation is the first step prior to any medical analysis. With the increase in modern disease variety, the images (specially cardiac magnetic resonance (CMR) images) to be segmented are found complex in nature. That might be due to noise, color geometry etc. Random walk method is proved to be good enough to this type of images. Simultaneously, it is robust noise and it does not require any pre-condition to perform. In the present paper we show the importance of weighting function, that is used in the method, on the algorithm output. This paper presents a new approach using difference of Gaussian (DoG) weighting function in the random walk method. We compare the frequently used Gaussian weighting function with DoG and show DoG to be the better one. Finally using DoG weighting function, the random walk method is performed on CMR data for left ventricle contour extraction. The result using DoG weighting function is found to be encouraging than that of Gaussian weighting function. Index Terms—Cardiac magnetic resonance image, Gaussian weighting function, difference of Gaussian weighting function, random walker.

Noise Effect on LV Image Segmentation

According to the basic knowledge of the information theory, noise is known to hinder signal quality, and as the noise level increases the signal detection sensitivity decreases. Noise has a detrimental effect on tasks involving vigilance, memory and divided attention. Its effects vary depending on the nature of the noise (including volume, predictability and perceived control) and the type of task that participants are asked to undertake. Rician noise introduces a bias into MRI measurements that can have a significant impact on the cardic magnetic resonance (CMR) image segmentation. Noting to the variations of the noise with the signal amplitude, this paper discusses the over all effect of noise on segmentation. The eigen values that describe the contrast of the image are shown to have been decreased with the addition of noise. The presence of heavy noise is shown to lead to an under segmentation. Index Terms—Cardiac magnetic resonance image, noise, eigen values, variogram, power spectral density.