A colour normalization method for giemsa-stained blood cell images

This paper presents a novel method for the colour normalization of Giemsa-stained peripheral blood cell images. The normalization is applied separately to the foreground and background regions. A rough estimation of the foreground-background regions is done by mathematical morphology and followed by a refined segmentation using histograms of these regions. Then an illumination independent response is calculated using the background region. The normalization is completed by transforming the foreground region according to a reference set. The proposed method has been tested on many images and has been found successful

Texture Analysis with Arbitrarily Oriented Morphological Opening and Closing

13 pagesThis paper presents a fast, streaming algorithm for 1-D morphological opening on 2-D support. The algorithm is further extended to compute the complete size distribution during a single image run. The Structuring Element (SE) can be oriented under arbitrary angle that allows us to perform different orientation-involved image analysis, such as local angle extraction, directional granulometries, \etc The algorithm processes an image in constant time irrespective of the SE orientation and size, with a minimal latency and very low memory requirements. Regardless the SE orientation, it reads and writes data strictly sequentially in the horizontal scan order. Aforementioned properties allow an efficient implementation in embedded hardware platforms that opens a new opportunity of a parallel computation, and consequently, a significant speed-up

One-Dimensional Openings, Granulometries and Component Trees in O(1) Per Pixel

International audienceWe introduce a new, efficient and adaptable algorithm to compute openings, granulometries and the component tree for one-dimensional (1-D) signals. The algorithm requires only one scan of the signal, runs in place in per pixel, and supports any scalar data precision (integer or floating-point data). The algorithm is applied to two-dimensional images along straight lines, in arbitrary orientations. Oriented size distributions can thus be efficiently computed, and textures characterized. Extensive benchmarks are reported. They show that the proposed algorithm allows computing 1-D openings faster than existing algorithms for data precisions higher than 8 bits, and remains competitive with respect to the algorithm proposed by Van Droogenbroeck when dealing with 8-bit images. When computing granulometries, the new algorithm runs faster than any other method of the state of the art. Moreover, it allows efficient computation of 1-D component trees