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Machine learning system for automated blood smear analysis

Grochowski Michał, Wąsowicz Michał, Mikołajczyk Agnieszka, Ficek Mateusz, Kulka Marek, Wróbel Maciej S., Jędrzejewska-Szczerska Małgorzata

Metrology and Measurement Systems

2019

Vol. 26, nr 1

81--93

In this paper the authors propose a decision support system for automatic blood smear analysis based onmicroscopic images. The images are pre-processed in order to remove irrelevant elements and to enhancethe most important ones – the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.

Decision fusion based on voting scheme for IR and visible face recognition

Shahbe M. D., Hati S.

Machine Graphics and Vision

2008

Vol. 17, No. 4

403-424

In this paper we present an evaluation study of decision fusion strategies for infrared (IR) and visible face recognition. Several decision fusion methods based on a voting scheme (minimization, product and averaging) are discussed, and experiments for various conditions of probe and gallery sets are performed on two databases with paired IR and visible face imageries. The Eigenfaces and Fisherface classification techniques are used to extract the face features, and the performance of fusion methods on both classification approaches is discussed.

Testing dimension reduction methods for image retrieval

Moravec P., Snášel V.

Image Processing & Communications

2006

Vol. 11, no 2

51-61

In this paper, we compare performance of several dimension reduction techniques, namely LSI, NMF, SDD and FastMap. The qualitative comparison is based on rank lists and evaluated on a collection of faces from the Olivetti Research Lab. We compare the quality of these methods from several standpoints: the visual impact, quality of generated "eigenfaces", size of reduced matrices and retrieval performance.