Edge Detection for Pattern Recognition: A Survey <s> Medical Imaging <s> We present a framework for rigid registration of a set of B-mode ultrasound images to a CT scan in the context of Radiotherapy planning. Our main focus is on deriving an appropriate similarity measure based on the physical properties and artifacts of ultrasound. A combination of a weighted Mutual Information term, edge correlation, clamping to the skin surface and occlusion detection is able to assess the alignment of structures in ultrasound images and simulated slices generated from the CT data. Hence a set of ultrasound images, whose relative transformations are given by a magnetic tracking device, can be registered automatically to the CT scan. We validated our methods on neck data of patients with head and neck tumors and cervical lymph node metastases. <s> BIB001 </s> Edge Detection for Pattern Recognition: A Survey <s> Medical Imaging <s> An essential processing step for comparison of Ultrasound Computer Tomography images to other modalities, ::: as well as for the use in further image processing, is to segment the breast from the background. In this ::: work we present a (semi-) automated 3D segmentation method which is based on the detection of the breast ::: boundary in coronal slice images and a subsequent surface fitting. The method was evaluated using a software ::: phantom and in-vivo data. The fully automatically processed phantom results showed that a segmentation of ::: approx. 10% of the slices of a dataset is sufficient to recover the overall breast shape. Application to 16 in-vivo ::: datasets was performed successfully using semi-automated processing, i.e. using a graphical user interface for ::: manual corrections of the automated breast boundary detection. The processing time for the segmentation of ::: an in-vivo dataset could be significantly reduced by a factor of four compared to a fully manual segmentation. ::: Comparison to manually segmented images identified a smoother surface for the semi-automated segmentation ::: with an average of 11% of differing voxels and an average surface deviation of 2mm. Limitations of the edge ::: detection may be overcome by future updates of the KIT USCT system, allowing a fully-automated usage of our ::: segmentation approach. <s> BIB002 </s> Edge Detection for Pattern Recognition: A Survey <s> Medical Imaging <s> This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases. <s> BIB003 </s> Edge Detection for Pattern Recognition: A Survey <s> Medical Imaging <s> To achieve the fast and accurate segmentation of ultrasound image, a novel edge detection method for speckle noised ultrasound images was proposed, which was based on the traditional Canny and a novel multiplicative gradient operator. The proposed technique combines a new multiplicative gradient operator of non-Newtonian type with the traditional Canny operator to generate the initial edge map, which is subsequently optimized by the following edge tracing step. To verify the proposed method, we compared it with several other edge detection methods that had good robustness to noise, with experiments on the simulated and in vivo medical ultrasound image. Experimental results showed that the proposed algorithm has higher speed for real-time processing, and the edge detection accuracy could be 75% or more. Thus, the proposed method is very suitable for fast and accurate edge detection of medical ultrasound images. <s> BIB004
Medical images contain several structural and texture details that relate to identifying the health of an individual (Ardeshir Goshtasby & Stavri, 2007; Bourne, 2010; Alex Pappachen James & Dasarathy, 2014; Singh & Khare, 2013 . Like object detection, the structural information can be used to segment areas with images created with different modalities: MRI (Laishram, Romesh, Kumar, Anshuman, & Prakash, 2014; Taghizadeh, Moslem, & Mahboobeh, 2011 , CT  , and ultrasound (Hopp, Zapf, & Ruiter, 2014; Leondes, 2003 . Gradient filters such as based on canny operator BIB004 , and active shape models (Arámbula Cosío, Acosta, & Edgar, 2015) find use in ultrasounds. In radiology images, use of knowledge  , curvatures  , surface fitting BIB002 , and gradients (Hopp et al., 2014; Zheng et al., 2015 are used in combination with edge detectors for implementing segmentation algorithms. Multimodal fusion of medical images also utilize several edge feature detection methods to combine and enhance the quality of features. The spatial structural cues (Jia, Huang, & Wang, 2014; Zheng et al., 2015 , clustering methods BIB003 , and image registration BIB001 are some of applications of edge features applied in image fusion at feature and decision levels.