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Guadalajara, Mexico

Osuna V.,CIC IPN | Cuevas E.,CUCEI UDEG | Sossa H.,CIC IPN
Advances in Intelligent Systems and Computing | Year: 2013

Acute lymphoblastic leukemia is a blood cancer that can be cured if it is detected at early stages; however, the analysis of smear blood by a human expert is tired and subject to errors. In such a sense, diagnostic of the disease is costly and time consuming. Considering that situation, several automatic segmentation methods have been proposed, some of them containing combinations of classic image analysis tools, as thresholding, morphology, color segmentation and active contours, only to mention some. In this paper is proposed the use of Hellinger distance as an alternative to Euclidean distance in order to estimate a Gaussian functions mixture that better fits a gray-level histogram of blood cell images. Two evolutionary methods (Differential Evolution and Artificial Bee Colony) are used to perform segmentation based on histogram information and an estimator of minimum distance. The mentioned techniques are compared with classic Otsu's method by using a qualitative measure of the resulting segmentation and ground-truth images. Experimental results show that the three methods performed almost in a similar fashion, but the evolutionary ones evaluate almost 75 % less the objective function compared with Otsu's. Also, was found that the use of a minimum distance estimator constructed with Hellinger distance and evolutionary techniques is robust and does not need a penalization factor as the needed when an Euclidean distance is used. © Springer-Verlag Berlin Heidelberg 2013. Source


Cuevas E.,CUCEI UDEG | Sossa H.,CIC IPN | Osuna V.,CIC IPN | Zaldivar D.,CUCEI UDEG | Perez-Cisneros M.,CUCEI UDEG
Advances in Intelligent Systems and Computing | Year: 2013

Automatic circle detection in digital images has received considerable attention over the last years. Recently, several robust circle detectors, based on evolutionary algorithms (EA), have been proposed. They have demonstrated to provide better results than those based on the Hough Transform. However, since EA-detectors usually need a large number of computationally expensive fitness evaluations before a satisfying result can be obtained; their use for real time has been questioned. In this work, a new algorithm based on the Harmony Search Optimization (HSO) is proposed to reduce the number of function evaluation in the circle detection process. In order to avoid the computation of the fitness value of several circle candidates, the algorithm estimates their values by considering the fitness values from previously calculated neighboring positions. As a result, the approach can substantially reduce the number of function evaluations preserving the good search capabilities of HSO. Experimental results from several tests on synthetic and natural images with a varying complexity range have been included to validate the efficiency of the proposed technique regarding accuracy, speed and robustness. © Springer-Verlag Berlin Heidelberg 2013. Source

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