International Journal of Medical Imaging 2017; 5(6): 63-69 http://www.sciencepublishinggroup.com/j/ijmi doi: 10.11648/j.ijmi.20170506.11 ISSN: 2330-8303 (Print); ISSN: 2330-832X (Online) A Systematic Survey and Evaluation of Blood Vessel Extraction Techniques Kalim Qureshi Department of Information Science, College of Computer Sciences and Engineering, Kuwait University, Kuwait, Kuwait Email address: kalimuddinqureshi@gmail.com To cite this article: Kalim Qureshi. A Systematic Survey and Evaluation of Blood Vessel Extraction Techniques. International Journal of Medical Imaging. Vol. 5, No. 6, 2017, pp. 63-69. doi: 10.11648/j.ijmi.20170506.11 Received: October 11, 2017; Accepted: October 31, 2017; Published: December 27, 2017 Abstract: The automatic extraction of brain vessels from Magnetic Resonance Angiography (MRA) has found its application in vascular disease diagnosis, endovascular operation and neurosurgical planning. In this paper we first present a concise methodology, pros & cons of well-known vessel extraction techniques. A systematic survey of latest development in the area of vessel extraction by using region growing algorithms is present. Then we detail the main challenges of vessel extraction and segmentation area. Based on review and our experience in the area, we finally present enhancement in region growing algorithm. Our proposed algorithm shows performance improvement as compare to traditional region growing algorithm. Keywords: Image Processing, Segmentation, Region Growing, Medical Imaging, Vessels, MRA 1. Introduction Segmentation is a process of partitioning an image into regions on the basis of homogeneity of desired features [1]. Segmentation plays key role in the field of medical imaging and is applied in numerous applications i.e. extraction of blood vessels, detection of tumors, image registration, atlas matching, surgical planning etc. [2]. Images obtained from segmentation are further used in medical applications like diagnosis of different diseases, treatment planning, study of anatomical structure and computer-integrated surgery [3]. Segmentation techniques are depended on the following factors: a). Imaging modality b). Application domain c). Manual, semiautomatic or automatic method d). Specific features Medical image segmentation is considered as a difficult task due to variable shapes of objects and different qualities of images causing noise. Although bundle of segmentation techniques have been developed [4-9] still there is no single segmentation technique that is applicable for all imaging applications. The most common region segmentation method is thresholding, which is most often used as an initial step in majority of image processing applications. According to this technique an image is partitioned into two categories of pixels on the basis of selected threshold [10, 11]. One category includes pixels with lesser values than the threshold and other contains pixels with values greater or equal to the threshold. Several techniques have been proposed for threshold selection [12, 13]. 2. Comparison of Vessels Extraction Techniques Segmentation plays a vital role in the diagnosis of vascular diseases. Segmentation techniques are categorized for both general applications and specifically for blood vessels extraction. According to [14] blood vessels segmentation algorithms are categorized as follows: a). Edge oriented techniques b). Region based techniques c). Active contour techniques d). Hybrid techniques 2.1. Segmentation Using Edge-Oriented Techniques Intensity values at edges of an image are very high as compared to other regions [15-17]. An abrupt change in intensities is noted at each edge point, which implies rate of