Abstract
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.
Highlights
The non-Hodgkin lymphoma is a diversified group of neoplasm that appears in the lymphocyte
We have presented a study of comparisons between the performance of enhancement filters for different noises in histological images
In our study we considered the classes of cancer in these images separately
Summary
The non-Hodgkin lymphoma is a diversified group of neoplasm that appears in the lymphocyte. This kind of lymphoma is estimated to happen in 10 out of 100 thousand people, but this number has been increasing about 4% yearly. According to [1], some of the reasons for this increase are the improvement in diagnostic methods, preciser morphological classifications and better health information systems. This neoplasm does not follow a characteristic histological pattern, and it grows with an altering cyclical behaviour. Diagnoses and prognoses are based upon histological analyses of the lymph nodes. The main pieces of information they collect are: arrangement and quantity of centrocytes and centroblasts, internal characteristics
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