Applying Enhancement Filters in the Pre-processing of Images of Lymphoma

3rd International Conference on Mathematical Modeling in Physical Sciences

物理学;数学

Silva, Sérgio Henrique^1 ; Do Nascimento, Marcelo Zanchetta^2 ; Neves, Leandro Alves^3 ; Batista, Valério Ramos^4

Faculty of Mechanical Engineering, Federal University of Uberlândia (UFU), MG, Uberlândia, Brazil^1

Faculty of Computer Science, Federal University of Uberlândia (UFU), MG, Uberlândia, Brazil^2

Institute of Biosciences, Literature and Exact Sciences, Department of Computer Science and Statistics, São Paulo State University (UNESP), São José do Rio Preto, SP, Brazil^3

Mathematics, Computer Science and Cognition Centre, Federal University of ABC (UFABC), Santo André, SP, Brazil^4

关键词: B cell chronic lymphocytic leukemia;    Colour models;    Digital image;    Histological images;    Pre-processing;    Pre-processing step;    Removing noise;    Structural similarity indices;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/574/1/012122/pdf
DOI  :  10.1088/1742-6596/574/1/012122

来源: IOP

【 摘 要 】

Lymphoma is a type of cancer that affects the immune system, and is classified as Hodgkin or non-Hodgkin. It is one of the ten types of cancer that are the most common on earth. Among all malignant neoplasms diagnosed in the world, lymphoma ranges from three to four percent of them. Our work presents a study of some filters devoted to enhancing images of lymphoma at the pre-processing step. Here the enhancement is useful for removing noise from the digital images. We have analysed the noise caused by different sources like room vibration, scraps and defocusing, and in the following classes of lymphoma: follicular, mantle cell and B-cell chronic lymphocytic leukemia. The filters Gaussian, Median and Mean-Shift were applied to different colour models (RGB, Lab and HSV). Afterwards, we performed a quantitative analysis of the images by means of the Structural Similarity Index. This was done in order to evaluate the similarity between the images. In all cases we have obtained a certainty of at least 75%, which rises to 99% if one considers only HSV. Namely, we have concluded that HSV is an important choice of colour model at pre-processing histological images of lymphoma, because in this case the resulting image will get the best enhancement.