Performance Analysis of Distance Transform Based Inter-Slice Similarity Information on Segmentation of Medical Image Series

Abstract

Abstract-Segmentation of organs from CT and MR image series is a challenging research area in all fields of medical imaging. Although, organs of interest are threedimensional in nature, slice-by-slice approaches are widely used in clinical applications because of their ease of integration with the current manual segmentation scheme (i.e. gold standard). Moreover, the high anisotropy of CT and MR data makes intra-slice information more reliable than inter-slice features. Nevertheless, slice-by-slice techniques should be supported with adjacent slice information since it is shown that features using the similarity of adjacent image slices outperform measures based on single-slice features in all cases. One of this similarity features is the distance transform which is shown to be effective on providing inter-slice similarity of abdominal organs. A parameter that control the vicinity of search area using the distance transform is α, which determines the order of the power of distance transforms applied to the image. Since there is no study discussing the effect of α on segmentation performance, the aim of this study is to analyze how changes on α affects performance in terms of accuracy, computation and time requirements. The simulations performed on several medical image series and for four different abdominal organs show the importance of parameter analysis for distance transformation.

Image Pattern Analysis With Image Potential Transform

“…DT-algorithm calculates distance from every foreground pixel to the nearest background pixel and assigns this value to the central pixel. Similarly, it calculates distance from background pixel to the nearest foreground pixel and assigns this value to the central pixel [24][25][26][27][28][29][30][31]. Source: own preparation At present time some DT-algorithms were generalized to three and more dimensions.…”

“…Source: own preparation At present time some DT-algorithms were generalized to three and more dimensions. It is rather important for medical image processing as medical images often are three dimensional or consist of many two-dimensional slices of three dimensional organs [30].…”

“…In medical imaging DT is one of best means for discovering the similarity between images. DT image transform is important for 3D study of inner organs using slice-by-slice method [30]. Good results were obtained using together watershed algorithm and DT for blood cell image segmentation [31].…”

Microstructural Analysis of Granular Metal-Ceramic Composite Materials of Matrix Type

“…Computer analysis may help obtain characteristics that are visually indistinguishable in the images. To study these characteristics, effective methods of analysis have been developed, such as the method of Markov random fields [11] or methods of coordinate image transformation [12][13][14][15]. Of great interest is the study of the fractal properties of materials and the cluster analysis of microphotographs.…”