A Comparative Study for String Metrics and the Feasibility of Joining them as Combined Text Similarity Measures

This paper aims to introduce an optimized Damerau–Levenshtein and dice-coefficients using enumeration operations (ODADNEN) for providing fast string similarity measure with maintaining the results accuracy; searching to find specific words within a large text is a hard job which takes a lot of time and efforts. The string similarity measure plays a critical role in many searching problems. In this paper, different experiments were conducted to handle some spelling mistakes. An enhanced algorithm for string similarity assessment was proposed. This algorithm is a combined set of well-known algorithms with some improvements (e.g. the dice-coefficient was modified to deal with numbers instead of characters using certain conditions). These algorithms were adopted after conducting on a number of experimental tests to check its suitability. The ODADNN algorithm was tested using real data; its performance was compared with the original similarity measure. The results indicated that the most convincing measure is the proposed hybrid measure, which uses the Damerau–Levenshtein and dicedistance based on n-gram of each word to handle; also, it requires less processing time in comparison with the standard algorithms. Furthermore, it provides efficient results to assess the similarity between two words without the need to restrict the word length

Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed