Enhanced Support Region for Scale-space Blob Detection
نویسندگان
چکیده
This paper presents a new criterion for blob detection applied in the framework of scale-space based segmentation of objects from digital medical images. The proposed method is based on fitting the blob to a standard shape, such as the Gaussian, subject to constraints based on the blob support area or the total variation. The method has been verified and compared with the conventional procedures using a variety of synthetic images as well as the real images used in medical diagnostics. The examples include detection of abnormalities in digital retinal images and microscope cell imagery.
منابع مشابه
A Novel Method for Generating Scale Space Kernels Based on Wavelet Theory
The linear scale-space kernel is a Gaussian or Poisson function. These functions were chosen based on several axioms. This representation creates a good base for visualization when there is no information (in advanced) about which scales are more important. These kernels have some deficiencies, as an example, its support region goes from minus to plus infinite. In order to solve these issues se...
متن کاملReduced-Reference Image Quality Assessment based on saliency region extraction
In this paper, a novel saliency theory based RR-IQA metric is introduced. As the human visual system is sensitive to the salient region, evaluating the image quality based on the salient region could increase the accuracy of the algorithm. In order to extract the salient regions, we use blob decomposition (BD) tool as a texture component descriptor. A new method for blob decomposition is propos...
متن کاملJunction Detection with Automatic Selection of Detection Scales and Localization Scales
The subject of scale selection is essential to many aspects of multi-scale and multi-resolution processing of image data. This article shows how a general heuristic principle for scale selection can be applied to the problem of detecting and localizing junctions. In a rst uncommitted processing step initial hypotheses about interesting scale levels (and regions of interest) are generated from s...
متن کاملClassification of Atomic Density Distributions Using Scale Invariant Blob Localization
We present a method to classify atomic density distributions using CCD images obtained in a quantum optics experiment. The classification is based on the scale invariant detection and precise localization of the central blob in the input image structure. The key idea is the usage of an a priori known shape of the feature in the image scale space. This approach results in higher localization acc...
متن کاملMean-shift Blob Tracking through Scale Space
The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing this scale or updating it while tracking blobs that are changing in size. In this paper, we adapt Lindeberg’s theory of feature scale selection based on local maxima of differential scale...
متن کامل