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1

An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

Malinowski Kamil, Saeed Khalid

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

391--403

EN

Pupil and iris segmentation based on ellipsis or circle recognition are sensitive to light reflections and reflected images. The method presented here is independent of size and shape and at the same time insensitive to light reflections and reflected mirror images. The pupil detected using the algorithm can be a reference point to further segmentation of the sclera of the eye as well as of the iris. The method is also effective when the pupil and iris are not positioned perpendicularly to the camera eye. The algorithm’s average segmentation accuracy for all tested databases was 96% when considering only noisy and distorted images whilst a result of 100% was achieved with unblurred and clear images. The proposed method can be quickly and simply reproduced with a combination of known image processing methods. The developed algorithm for detecting the eyelid boundaries is effective with noisy and poor quality images due to the use of edge approximation using the Harmony Search Algorithm. An optimized shape detection method was used to detect the pupil and its edges. A method based on the variation and the average was used to eliminate shadows and eyelashes. The proposed scheme was tested on the UBIRIS.v1 database, MMU.v1 database and MILES databases, providing high results and short segmentation time. Segmentation accuracy for UBIRIS.v1 was 98.14%, for MMU.v1 – 90% and for MILES – 99.8%.

2

New image descriptor from edge detector and blob extractor

Grycuk R., Scherer R., Gabryel M.

Journal of Applied Mathematics and Computational Mechanics

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2015

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Vol. 14, nr 4

31--39

EN

In this paper we present a novel approach for image description. The method is based on two well-known algorithms: edge detection and blob extraction. In the edge detection step we use the Canny detector. Our method provides a mathematical description of each object in the input image. On the output of the presented algorithm we obtain a histogram, which can be used in various fields of computer vision. In this paper we applied it in the content-based image retrieval system. The simulations proved the effectiveness of our method.

3

A connected component labelling algorithm for greyscale mammography image processing as a pre-processing tool

Yapa R. D., Harada K.

Machine Graphics and Vision

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2007

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Vol. 16, No. 3/4

305-327

EN

A new algorithm for connected component-labelling is presented in this paper. The proposed algorithm requires only one scan through an image for labelling connected components. Once this algorithm encounters a starting pixel of a component, it traces in full all the contour pixels and all internal pixels of that particular component. The algorithm recognizes components of the image one at a time while scanning in the raster order. This property will be useful in areas such as image matching, image registration, content-based information retrieval and image segmentation. It is also capable of extracting the contour pixels of an image and storing them in a clock-wise directional order, which will provide useful information in many applications. The algorithm assigns consecutive label numbers to different components, and therefore requires a minimum number of labels. We have used the algorithm in mammography image processing as a pre-processing tool, and have demonstrated the possibility of using it for breast tissue segmentation and for detecting regions of interest in breast tissue. Another important advantage of the algorithm is that it can be used as a content-based image retrieval tool for retrieving images based on the visual contents of a given image. This would be very useful in retrieving related images from large scale medical databases.