Local isotropic phase symmetry measure for detection of beta cells and lymphocytes

Abstract

Diabetes can be associated with a reduction in functional β cell mass, which must be restored if the disease is to be cured or progress is to be arrested. To study the cell count, it is also necessary to determine the number of nuclei within the insulin stained area. It can take a single experimentalist several months to complete a single study of this kind, results of which may still be quite subjective. In this paper, we propose a framework based on a novel measure of local symmetry for detection of cells. The local isotropic phase symmetry measure (LIPSyM) is designed to give high values at or near the cell centers. We demonstrate the effectiveness of our algorithm for detection of two types of specific cells in histology images, cells in mouse pancreatic sections and lymphocytes in human breast tissue. Experimental results for these two problems show that our algorithm performs better than human experts for the former problem, and outperforms the best reported results for the latter.

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

“…It takes into account uncertainty in detection of the center location, as well as, variability in the appearance of nuclei. In our experiments (Section VII-C), the NEP (17) shows higher classification performance than that of the single-patch predictor (15).…”

“…Al Kofahi et al [14] proposed a graph cutbased method that is initialized using response of the image to Laplacian of Gaussian (LoG) filter. Kuse et al [15] employed local phase symmetry to detect bilaterally symmetrical nuclei. Similarly, Veta et al [16] relied on the direction of gradient to identify the center of nuclei.…”

Learning to Detect Cells Using Non-overlapping Extremal Regions

“…Table 1 compares our experimental results (S+I method) on the histopathology data set to the methods presented in the ICPR 2010 contest, and to [6] and [1], published since then. The overall comparison is favourable to our method, with a considerable improvement on precision and recall over all other methods.…”

Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

“…A set of Gabor filters with different frequencies and orientations may be helpful for extracting useful features from an image. The local isotropic phase symmetry measure (LIPSyM) of Kuse et al (2011) takes the discrete Fourier transform of the input image, and filters this frequency information through a bank of Gabor filters. Kuse et al (2011) also note that local responses of each Gabor filter can be represented in terms of energy and amplitude.…”

“…The local isotropic phase symmetry measure (LIPSyM) of Kuse et al (2011) takes the discrete Fourier transform of the input image, and filters this frequency information through a bank of Gabor filters. Kuse et al (2011) also note that local responses of each Gabor filter can be represented in terms of energy and amplitude. Thus, Gabor features, such as the mean-squared energy and the mean amplitude, can be computed through the phase symmetry measure for a bank of Gabor filters with various scales and rotations.…”