Follicle Detection in Ultrasound Images using Adaptive Clustering Algorithms and Empirical Mode Decomposition
J.Harikiran1, E.Vamsidhar2, B.Srinivasa Rao3, B.Saichandana4
1Dr.J.Harikiran*, Assistant professor, School of CSE, Vellore Institute of Technology (VIT), VIT-AP, Amaravathi.
2Dr.B.Srinivasa Rao, Associate Professor, School of CSE, Vellore Institute of Technology, VIT-AP, Amaravathi.
3Dr.E.Vamsidhar, Associate Professor, Department of CSE, KLEF, KL University, Vijayawada.
4Dr.B.Saichandana, Associate Professor, Department of CSE, KLEF, KL University, Vijayawada.
Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 3137-2141 | Volume-9 Issue-2, December 2019. | Retrieval Number: A4013119119/2019©BEIESP | DOI: 10.35940/ijitee.A4013.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ultrasound Imaging is one of the techniques used to study inside human body with images generated using high frequency sounds waves. The applications of ultrasound images include examination of human body parts such as Kidney, Liver, Heart and Ovaries. This paper mainly concentrates on ultrasound images of ovaries.Monitoring of follicle is important in human reproduction. This paper presents a method for follicle detection in ultrasound image of ovaries using Adaptive data clustering algorithms. The main requirements for any clustering algorithm are to initialize the value of K, i.e. the number of clusters. Estimating this K value is difficult task for given data. This paper presents adaptive data clustering algorithm which generates accurate segmentation results with simple operation and avoids the interactive input K (number of clusters) value for segmentation. The results represent adaptive data clustering algorithms are better than normal algorithms for clustering in ultrasound image segmentation. After segmentation, using the region properties of the image, the follicles in the ovary image are identified. The proposed algorithm is tested on sample ultrasound images of ovaries for identification of follicles and with the region properties, the ovaries are classified into categories, normal, cystic and polycystic ovary with its geometric properties. Keywords: Ovarian Classification,
Keywords: Image Processing, Histogram Equalization, Bi-dimensional Empirical Mode Decomposition.
Scope of the Article: Image Processing and Pattern Recognition