Counting of RBCs and WBCs in noisy normal blood smear microscopic images

This work focuses on the segmentation and counting of peripheral blood smear particles which plays a vital role in medical diagnosis. Our approach profits from some powerful processing techniques. Firstly, the method used for denoising a blood smear image is based on the Bivariate wavelet. Secondly, image edge preservation uses the Kuwahara filter. Thirdly, a new binarization technique is introduced by merging the Otsu and Niblack methods. We have also proposed an efficient step-by-step procedure to determine solid binary objects by merging modified binary, edged images and modified Chan-Vese active contours. The separation of White Blood Cells (WBCs) from Red Blood Cells (RBCs) into two sub-images based on the RBC (blood’s dominant particle) size estimation is a critical step. Using Granulometry, we get an approximation of the RBC size. The proposed separation algorithm is an iterative mechanism which is based on morphological theory, saturation amount and RBC size. A primary aim of this work is to introduce an accurate mechanism for counting blood smear particles. This is accomplished by using the Immersion Watershed algorithm which counts red and white blood cells separately. To evaluate the capability of the proposed framework,experiments were conducted on normal blood smear images. This framework was compared to other published approaches and found to have lower complexity and better performance in its constituent steps; hence, it has a better overall performance

Application of pattern recognition techniques for the analysis of thin blood smear images

In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of histopathological images. We focus on counting of Red and White blood cells using microscopic images of blood smear samples. We provide literature survey and point out new challenges. We present an improved cell counting algorithm

Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies

According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers

Web–based framework for breast cancer classification

The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector. The feature vector was introduced as an input to 8 classifiers and their accuracy was checked. The results show that the highest classification accuracy of 89.02 % was recorded for the multilayer perceptron. Fuzzy c–means proved to be the most accurate segmentation algorithm, but at the same time it is the most computationally intensive among the three studied segmentation methods

GLCM and GLRLM based texture features for computer-aided breast cancer diagnosis

This paper presents 15 texture features based on GLCM (Gray-Level Co-occurrence Matrix) and GLRLM (Gray-Level Run-Length Matrix) to be used in an automatic computer system for breast cancer diagnosis. The task of the system is to distinguish benign from malignant tumors based on analysis of fine needle biopsy microscopic images. The features were tested whether they provide important diagnostic information. For this purpose the authors used a set of 550 real case medical images obtained from 50 patients of the Regional Hospital in Zielona Góra. The nuclei were isolated from other objects in the images using a hybrid segmentation method based on adaptive thresholding and kmeans clustering. Described texture features were then extracted and used in the classification procedure. Classification was performed using KNN classifier. Obtained results reaching 90% show that presented features are important and may significantly improve computer-aided breast cancer detection based on FNB images

Partitioning a Regular n-gon into n+1 Convex Congruent Pieces is Impossible, for Sufficiently Large n

Proceedings of the 18th Annual Canadian Conference on Computational Geometry, August 14-16, 2006, Queen's University, Ontario, Canadainfo:eu-repo/semantics/publishe

Partitioning a Polygon into Two Mirror Congruent Pieces

info:eu-repo/semantics/publishe