Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.

Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it to assess bone age in young children. Our proposed technique can detect the boundaries of carpal bones in X-ray images by using the information from the vector image model and the edge map. Feature analysis of the carpal bones can reveal the important information for bone age assessment. Five features for bone age assessment are calculated from the boundary extraction result of each carpal bone. All features are taken as input into the support vector regression (SVR) that assesses the bone age. We compare the SVR with the neural network regression (NNR). We use 180 images of carpal bone from a digital hand atlas to assess the bone age of young children from 0 to 6 years old. Leave-one-out cross validation is used for testing the efficiency of the techniques. The opinions of the skilled radiologists provided in the atlas are used as the ground truth in bone age assessment. The SVR is able to provide more accurate bone age assessment results than the NNR. The experimental results from SVR are very close to the bone age assessment by skilled radiologists.

This paper presents a novel edge following technique for image segmentation designed to segment the left ventricle in cardiac magnetic resonance (MR) images. This is a required step to determine the volume of left ventricle in a cardiac MR image, which is an essential tool for cardiac diagnosis. The traditional method for extracting them from cardiac MR images is by human delineation. This method is accuracy but time consuming. So a new ventricular segmentation technique is proposed in order to reduce the analysis time with similar accuracy level compared to doctors' opinions. Our proposed technique can detect ventricle edges in MR images using the information from the vector image model and the edge map. We also compare the proposed segmentation technique with the active contour model (ACM) and the gradient vector flow (GVF) by using the opinions of two skilled doctors as the ground truth. The experimental results show that our technique is able to provide more accurate segmentation results than the classical contour models and visually close to the manual segmentation by the experts. The results evaluated using a numerical measure by mean of the probability of error in image segmentation also confirm the visual evaluation.

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