Regmentation: A New View of Image Segmentation and Registration

Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications

An efficient method for stamps recognition using Haar wavelet sub-bands

The problem facing certain organizations such as insurance companies and government institutions where a huge amount of documents is handled every day, hence an automated stamp recognition system is required. The image of the stamp may be on a different background, with different sizes, and suffers from rotating in different directions, also, the appearance of soft areas (patches) or small points as noise. Thus, the main objective of this paper is to extract and recognize the color stamp image. This paper proposed a method to recognize stamps, by using a technique named Haar wavelet sub-bands. The devised method has four stages: 1) extracts the stamp image; 2) preprocessing the image; 3) feature extraction; and 4) matching. This paper is implemented using C sharp (Microsoft Visual Studio 2012) programming language. The experiments conducted on a stamp dataset showed that the proposed method has a great capability to recognize stamps when using Haar wavelet transform with two sets of features (i.e., 100% recognition rate for energy features and 99.93% recognition rate for low order moment)

A Successful Broad-band Survey for Giant Lya Nebulae I: Survey Design and Candidate Selection

Giant Lya nebulae (or Lya "blobs") are likely sites of ongoing massive galaxy formation, but the rarity of these powerful sources has made it difficult to form a coherent picture of their properties, ionization mechanisms, and space density. Systematic narrow-band Lya nebula surveys are ongoing, but the small redshift range covered and the observational expense limit the comoving volume that can be probed by even the largest of these surveys and pose a significant problem when searching for such rare sources. We have developed a systematic search technique designed to find large Lya nebulae at 2<z<3 within deep broad-band imaging and have carried out a survey of the 9.4 square degree NOAO Deep Wide-Field Survey (NDWFS) Bootes field. With a total survey comoving volume of ~10^8 h^-3_70 Mpc^3, this is the largest volume survey for Lya nebulae ever undertaken. In this first paper in the series, we present the details of the survey design and a systematically-selected sample of 79 candidates, which includes one previously discovered Lya nebula.Comment: Accepted to ApJ after minor revision; 25 pages in emulateapj format; 18 figures, 3 table