Combination of noise removal and contrast enhancement methods for the preprocessing of mammogram images - towards the detection of breast cancer

• Autor(a) principal: Senthilkumar, B
• Data de Publicação: 2017
• Outros Autores: Gowrishankar, R., Vaishnavi, M., Gokila, S.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Bioscience journal (Online)
• Texto Completo: https://seer.ufu.br/index.php/biosciencejournal/article/view/36020
• topic: Breast cancer; CAD; Preprocessing; USFFT; MLRM; Mammogram; Health Sciences

Breast cancer is a major killer disease for women and men. It can be treated and controlled only if it is detected at its earlier stage. Early detection can be achieved by the help of Computer Aided Detection (CAD) methods. From the detailed study on previous researches, it is found that, there is no system producing 100% accuracy because of one or more reasons. Absence of effective preprocessing is the discussed reason that obstructs the detection accuracy of CAD method. Noise removal and contrast enhancement are the two types of preprocessing. There is no system performs both the preprocessing on mammogram image. This work is an attempt to develop an enhanced preprocessing method for CAD of breast cancer by incorporating suitable noise reduction and contrast enhancement methods in the conventional CAD system. Among the available noise reduction techniques, Fast Discrete Curvelet Transform (FDCT) based UnequiSpaced Fast Fourier Transform (USFFT) has been utilized and the Modified Local Range Modification (MLRM) technique has been utilized for contrast enhancement. Contrast enhancement after noise reduction double enhances the mammogram image and the proposed methods MSE value for the mammogram image mdb072 has been 1.44% reduced when comparing to the LRM method. Reduction in MSE increases the PSNR to 0.16%. Many mammogram images have been tested and the result shows that, increase in contrast, decrease in mean square error and increase in peak signal to noise ratio when comparing to existing methods.