A Regions of Confidence Based Approach to Enhance Segmentation with Shape Priors

©2010 SPIE - Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Presented at Computational Imaging VIII, January 17, 2010, San Jose, CA.http://dx.doi.org/10.1117/12.850888We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in

A Review of Left Ventricular Myocardium Analysis and Diagnosis Techniques for CT Images of Heart

Abstract Cardiovascular diseases associated with the left ventricle are main reason of deaths in heart diseases. Early diagnosis using advanced technologies will definitely aid in saving many lives. Cardiac computed tomography (CT) images are one of the tools for this function. Automatic segmentation of left ventricular myocardium is carried out from cardiac CT images. The system uses a iterative strategy for localization of left ventricle followed by deformation of myocardial surface to obtain refine segmentation i.e. blood pool surface of the CT image is extracted and triangulated surface is taken as an area of interest. Geometric characterization of triangulated surface gave precise localization of left ventricle. Subsequently, initialization of epicardial and endocacardial masks is done and myocardial wall is extracted. This paper gives review of different techniques used for segmentation revealed in previously reported literature along with the proposed technology. The proposed system is expected to work based on the standard rules defined by medical experts for disease diagnosis are yet to define

Automatically Extract the Left and Right Ventricular Myocardium from CT Images by Using Region Based Segmentation

ABSTRACT: Seeded Region Growing is the more gorgeous method in medical image segmentation by using high level information of images for selecting seeds for segmentation. This SRG algorithm that provides the most efficient for fixing the labels for pixels and also for segmentation. This paper represents the automatic system for segmenting the left and right ventricular myocardium CT images by using Region growing method. This algorithm follows the focus on fixing the labels for region growing segmentation. This algorithm that gives the most accurate segmentation of Myocardium from CT images the delineation of the myocardial wall which is a exacting task due to large differences in myocardial shapes and quality of an image. In this paper, we describe an automatic method for extracting the myocardium from the left and right ventricles from CT images. In the method, the left and right ventricles are detected, by first identifying the endocardium and epicardium and then segmenting the myocardium. After that, a seed regiongrowing method is applied to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result, which gives the high accuracy

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%