Automatic Segmentation of Optic Disc using Modified Multi-level Thresholding

被引:0
|
作者
Kankanala, Mila [1 ]
Kubakaddi, Sanjeev [2 ]
机构
[1] SSN Coll Engn, Dept Elect & Commun, Kalavakkam, Tamil Nadu, India
[2] Itie Knowledge Solut, Bangalore, Karnataka, India
关键词
Optic disc segmentation; Image Processing; Diabetic Retinopathy; Glaucoma; Cup-to-disc ratio; Bioinformatics; IMAGE SEGMENTATION;
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Diabetic retinopathy and glaucoma are chronic eye diseases that lead to blindness if not diagnosed and treated in time. In the study of the different stages of Diabetic Retinopathy (DR) and Glaucoma, the use of retinal image analysis has become indispensable. The structure of the optic disc, the cup-to-disc ratio (CDR) and the thickness of the blood vessels are indicators of the condition of the eye. This paper proposes a novel method of automated OD (optic disc) segmentation using a modified multi-thresholding technique on a pre-processed fundus image. The algorithm we propose proceeds through three main steps: Pre-processing on the fundus image, followed by segmentation using the proposed novel multi-level Thresholding method and finally using morphological operations over the processed image to obtain the segmented results. The results from this method of segmentation are compared with those obtained using K-means clustering algorithm and the superiority of our method is demonstrated.
引用
收藏
页码:125 / 130
页数:6
相关论文
共 50 条
  • [31] Automatic Optic Disc segmentation using maximum intensity variation
    Kumar, Vivek
    Sinha, Neelam
    2013 IEEE TENCON SPRING CONFERENCE, 2013, : 29 - 33
  • [32] Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation
    Abualigah, Laith
    Al-Okbi, Nada Khalil
    Awwad, Emad Mahrous
    Sharaf, Mohamed
    Daoud, Mohammad Sh.
    EVOLVING SYSTEMS, 2024, 15 (04) : 1399 - 1426
  • [33] Multi-level Thresholding Segmentation Approach Based on Spider Monkey Optimization Algorithm
    Pal, Swaraj Singh
    Kumar, Sandeep
    Kashyap, Manish
    Choudhary, Yogesh
    Bhattacharya, Mahua
    PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION TECHNOLOGIES, IC3T 2015, VOL 2, 2016, 380 : 273 - 287
  • [34] Automated Optic Disc region location from fundus images: Using local multi-level thresholding, best channel selection, and an Intensity Profile Model
    Uribe-Valencia, Laura J.
    Martinez-Carballido, Jorge F.
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2019, 51 : 148 - 161
  • [35] Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding
    Laith Abualigah
    Nada Khalil Al-Okbi
    Saleh Ali Alomari
    Mohammad H. Almomani
    Sahar Moneam
    Maryam A. Yousif
    Vaclav Snasel
    Kashif Saleem
    Aseel Smerat
    Absalom E. Ezugwu
    Scientific Reports, 15 (1)
  • [36] Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm
    Zi, Jianjun
    Liu, Tao
    Zhang, Wei
    Pan, Xiaohua
    Ji, Hu
    Zhu, Honghu
    JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING, 2024, 16 (10) : 4285 - 4299
  • [37] Automatic Initialization of Level Set Segmentation for Application to Optic Disc Margin Identification
    Echegaray, Sebastian
    Soliz, Peter
    Luo, Wenbin
    2009 22ND IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, 2009, : 390 - +
  • [38] Multi-level thresholding: Maximum entropy approach using ICM
    Luo, XP
    Tian, J
    15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS: IMAGE, SPEECH AND SIGNAL PROCESSING, 2000, : 778 - 781
  • [39] Three level automatic segmentation of optic disc using LAB color space contours and morphological operation
    Prakash, Shree
    Kakarla, Jagadeesh
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2023, 33 (05) : 1796 - 1813
  • [40] Multi-level contour segmentation using multiple segmentation primitives
    Sluzek, A
    FOURTEENTH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1 AND 2, 1998, : 741 - 743