TY - GEN
T1 - Automatic Contrast Enhancement with Differential Evolution for Leukemia Cell Identification
AU - Ochoa-Montiel, R.
AU - Flores-Castillo, O.
AU - Sossa, Humberto
AU - Olague, Gustavo
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Image enhancement techniques are needed to decrease the negative effects of blur or unwanted noise in image processing. In biomedical images, the quality of images is very important to achieve an adequate identification to detection or diagnosis purposes. This paper addresses the use of contrast enhancement to facilitate the identification of leukemia in blood cell images. Differential evolution algorithm is used to get parameters required to apply contrast enhancement specifically in the interest region in the image, which facilites the posterior identification of leukemic cells. Identification of leukemic cells is accomplished applying an edges extraction and dilatation. From this image, two types of neural networks are used to classify the cells like healthy or leukemic cells. In first experiment, a multilayer perceptron is trained with the backpropagation algorithm using geometric features extracted from image. While in the second, convolutional networks are used. A public dataset of 260 healthy and leukemic cell images, 130 for each type, is used. The proposed contrast enhancement technique shows satisfactory results when obtaining the interest region, facilitating the identification of leukemic cells without additional processing, like image segmentation. This way, computational resources are decreased. On the other hand, to identify the cell type, images are classified using neural networks achieving an average classification accuracy of $$99.83\%$$.
AB - Image enhancement techniques are needed to decrease the negative effects of blur or unwanted noise in image processing. In biomedical images, the quality of images is very important to achieve an adequate identification to detection or diagnosis purposes. This paper addresses the use of contrast enhancement to facilitate the identification of leukemia in blood cell images. Differential evolution algorithm is used to get parameters required to apply contrast enhancement specifically in the interest region in the image, which facilites the posterior identification of leukemic cells. Identification of leukemic cells is accomplished applying an edges extraction and dilatation. From this image, two types of neural networks are used to classify the cells like healthy or leukemic cells. In first experiment, a multilayer perceptron is trained with the backpropagation algorithm using geometric features extracted from image. While in the second, convolutional networks are used. A public dataset of 260 healthy and leukemic cell images, 130 for each type, is used. The proposed contrast enhancement technique shows satisfactory results when obtaining the interest region, facilitating the identification of leukemic cells without additional processing, like image segmentation. This way, computational resources are decreased. On the other hand, to identify the cell type, images are classified using neural networks achieving an average classification accuracy of $$99.83\%$$.
KW - Contrast enhancement
KW - Differential evolution
KW - Leukemia cells
UR - http://www.scopus.com/inward/record.url?scp=85068333883&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-21077-9_26
DO - 10.1007/978-3-030-21077-9_26
M3 - Contribución a la conferencia
AN - SCOPUS:85068333883
SN - 9783030210762
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 282
EP - 291
BT - Pattern Recognition - 11th Mexican Conference, MCPR 2019, Proceedings
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Olvera-López, José Arturo
A2 - Salas, Joaquín
PB - Springer Verlag
T2 - 11th Mexican Conference on Pattern Recognition, MCPR 2019
Y2 - 26 June 2019 through 29 June 2019
ER -