boundary detection

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 58, NO. 3, MARCH 2011 567 Boundary Detection in Medical Images Using Edge Following Algorithm Based on Intensity Gradient and Texture Gradient Features Krit Somkantha, Nipon Theera-Umpon* , Senior Member , IEEE , and Sansanee Auephanwiriyakul , Senior Member , IEEE Abstract—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 conto ur model s, active conto urs with out edge s, gradi ent vect or 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. Index T erms—Bou ndary extra ctio n, edge dete ctio n, edge foll ow- ing, image segmentation, vector field model. I. INTRODUCTION I MAGE segmentation is an initial step before performing high-level tasks such as object recognition and understand- ing. Image segmentation is typically used to locate objects and boundaries in images. In medical imaging, segmentation is im- portant for feature extraction, image measurements, and im- Manuscript received June 22, 2010; revised October 3, 2010 and October 28, 2010; accepted October 29, 2010. Date of publication November 9, 2010; date of current version February 18, 2011. This work was supported in part by a grant under the program Strategic Scholarships for Frontier Research Network for Ph.D. Program Thai Doctoral degree from the Commission on Higher Edu- cation, Thailand. Asterisk indicates corresponding author. K. Somkantha is with the Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand (e-mail: [email protected]). *N. Theer a-Ump on is with the Depar tmen t of Elect rical Engin eerin g, Facu lty of Engi neeri ng, and the Biomedica l Engin eerin g Cent er, Chia ng Mai Uni vers ity , Chiang Mai 50200, Thailand (e-mail: [email protected]). S. Auephanwiriyakul is with the Department of Computer Engineering, Fac- ulty of Engineering, and the Biomedical Engineering Center, Chiang Mai Uni- versity , Chiang Mai 50200, Thailand (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplo re.ieee.org. Digital Object Identifier 10.1109/TBME.2010.20 91129 age display. In some applications it may be useful to extract boundaries of objects of interest from ultrasound images [1], [2], microscopic images [3]–[5], magnetic resonance (MR) images [6]–[8], or computerized tomography (CT) images [9], [10]. Segmentation techniques can be divided into classes in many ways, depending on the classification scheme. The most commonly used segmentation techniques can be categorized into two classes, i.e., edge-based approaches and region-based approaches. The strategy of edge-based approaches is to detect the objec t bound aries by using an edge detecti on opera tor and then extract boundaries by using the edge information. The problem of edge detection is the presence of noise that results in random variation in level from pixel to pixel. Therefore, the ideal edges are never encountered in real images [11], [12]. A great diversity of edge detection algorithms have been devised with differences in their mathematical and algorithmic properties such as Rob ert s, Sobel, Prewitt, La pla cia n, and Can ny , all of which are based on the difference of gray levels [13]–[16]. The difference of gray levels can be used to detect the discontinu- ity of gray levels. On the other hand, region-based approaches are based on similarity of regional image data. Some of the more widely used approaches are thresholding, clustering, region growing, and splitting and merging [17]. However, the performance evaluation of image segmentation results is still a challe ngi ng pro ble m as the y fai l to extra ct the cor rec t bounda rie s of objects in noisy images. In rec ent yea rs, there ha ve bee n se ve ral ne w met hods to solve the problem of boundary detection, e.g., active contour model (ACM), geodesic active contour (GAC) model, active contours without edges (ACWE), gradient vector flow (GVF) snake model, vector field convolution (VFC) snake model, etc. The snake models have become popular especially in boundary detection where the problem is more challenging due to the poor quality of the images. The ACMs also known as snakes are curves defined within an image domain that can be moved under the influe nce of the internal ene rgy and exter nal energy [18]–[20]. The internal energy is designed to keep the model smooth during deformation. The external energy is designed to move the model toward an object boundary or other desired features within an image. Howeve r, the snake has weak- nesses and limitations of small capture range and difficulties progressing into concave boundary regions. The GAC model is an extension of the ACM by taking into account of the geo- metric information of an image [21]. An ACM based on curve evolution and level sets, namely the ACWE, can detect objects’ 0018-9294/$ 26.00 © 2011 IEEE

boundary detection

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