Incorporating Index of Fuzziness and Adaptive Thresholding for Image Segmentation

Umi Salamah (Institut Teknologi Sepuluh Nopember Universitas Sebelas Maret)
Riyanarto Sarno (Institut Teknologi Sepuluh Nopember)
Agus Zainal Arifin (Institut Teknologi Sepuluh Nopember)
Anto Satriyo Nugroho (Center for Information and Communication Technology Agency for the Assessment and Application of Technology (PTIK-BPPT))
Ismail Eko Prayitno Rozi (Eijkman Institute for Molecular Biology)
Puji Budi Setia Asih (Eijkman Institute for Molecular Biology)

International Journal of Electrical and Computer Engineering
Vol 8, No 4: August 2018

Binary Segmentation of an image played an important role in many image processing application. An image that was having no bimodal (or nearly) histogram accompanied by low-contrast was still a challenging segmentation problem to address. In this paper, we proposed a new segmentation strategy to images with very irregular histogram and had not significant contrast using index of fuzziness and adaptive thresholding. Index of fuzziness was used to determine the initial threshold, while adaptive thresholding was used to refine the coarse segmentation results. The used data were grayscale images from related papers previously. Moreover, the proposed method would be tested on the grayscale images of malaria parasite candidates from thickblood smear that had the same problem with this research. The experimental results showed that the proposed method achieved higher segmentation accuracy and lower estimation error than other methods. The method also effective proven to segment malaria parasite candidates from thickblood smears image.