Morphological Image Processing for Evaluating Malaria Disease
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Segmentation and border identification of cells in images of peripheral blood smear slides
ACSC '07 Proceedings of the thirtieth Australasian conference on Computer science - Volume 62
Blood cell identification and segmentation by means of statistical models
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Improved wavelet-based microscope autofocusing for bloodsmears by using segmentation
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Automatic fuzzy-neural based segmentation of microscopic cell images
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Cell microscopic segmentation with spiking neuron networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Cytology imaging segmentation using the locally constrained watershed transform
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
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This work describes a part of a malarial image processing system for detecting and classifying malaria parasites in images of Giemsa stained blood slides in order to evaluate the parasitaemia of the blood. A major requirement of the system is an efficient method to segment cell images. This paper introduces a morphological approach to cell image segmentation more accurate than the classical watershed-based algorithm. We have applied grey scale granulometries based on opening with disk-shaped elements, flat and non-flat. We have used a non-flat disk-shaped structuring element to enhance the roundness and the compactness of the red cells improving the accuracy of the classical watershed algorithm, while we have used a flat disk-shaped structuring element to separate overlapping cells. These methods make use of knowledge of the red blood cell structure that is not used in existing watershed-based algorithms.