TY - JOUR
T1 - Incorporating index of fuzziness and adaptive thresholding for image segmentation
AU - Salamah, Umi
AU - Sarno, Riyanarto
AU - Arifin, Agus Zainal
AU - Nugroho, Anto Satriyo
AU - Rozi, Ismail Ekoprayitno
AU - Asih, Puji Budi Setia
N1 - Publisher Copyright:
Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
KW - Adaptive thresholding
KW - Bimodal
KW - Index of fuzziness
KW - Significant contrast
KW - Very irregular histogram
UR - http://www.scopus.com/inward/record.url?scp=85060489581&partnerID=8YFLogxK
U2 - 10.11591/ijece.v8i4.pp2406-2418
DO - 10.11591/ijece.v8i4.pp2406-2418
M3 - Article
AN - SCOPUS:85060489581
SN - 2088-8708
VL - 8
SP - 2406
EP - 2418
JO - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
IS - 4
ER -