Implementation of Fuzzy Gamma Adaptive Histogram Equalization for Penicillium and Aspergillus Species

Abstract

This paper proposes a new approach in image enhancement method to enhance the structure of fungi namely, Penicillium and Aspergillus. The new approach combines the methods of adaptive histogram equalization (AHE), gamma correction and fuzzy logic. Previous methods of enhancing the structure of fungi is barely available as the current conventional method of identifying a fungi species relies on detecting the fungi directly from a microscope instead of examining it in the form of an image. It is widely known that microscopic images of fungi are usually low in contrast. Plus, the structure of fungi is complex and changes according to its level of maturity. Thus, a new approach in image enhancement is investigated to improve the appearance of the structure of fungi. The performance of the proposed method is evaluated on the collected database consisting of 194 microscopic coloured image of fungi. The result obtained shows that the proposed method, Fuzzy Gamma Adaptive Histogram Equalization (FGAHE) has a better performance in terms of enhancing the structure of fungi by obtaining a PSNR value of 14.28 dB, 20.37 dB and 21.60 dB from the species of Penicillium, Aspergillus fumigatus and Aspergillus terreus respectively.