91 research outputs found
Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique
This paper presents an automated classification method of infective and
non-infective diseases from anterior eye images. Treatments for cases of
infective and non-infective diseases are different. Distinguishing them from
anterior eye images is important to decide a treatment plan. Ophthalmologists
distinguish them empirically. Quantitative classification of them based on
computer assistance is necessary. We propose an automated classification method
of anterior eye images into cases of infective or non-infective disease.
Anterior eye images have large variations of the eye position and brightness of
illumination. This makes the classification difficult. If we focus on the
cornea, positions of opacified areas in the corneas are different between cases
of the infective and non-infective diseases. Therefore, we solve the anterior
eye image classification task by using an object detection approach targeting
the cornea. This approach can be said as "anatomical structure focused image
classification". We use the YOLOv3 object detection method to detect corneas of
infective disease and corneas of non-infective disease. The detection result is
used to define a classification result of a image. In our experiments using
anterior eye images, 88.3% of images were correctly classified by the proposed
method.Comment: Accepted paper as a poster presentation at SPIE Medical Imaging 2020,
Houston, TX, US
Delineation of line patterns in images using B-COSFIRE filters
Delineation of line patterns in images is a basic step required in various
applications such as blood vessel detection in medical images, segmentation of
rivers or roads in aerial images, detection of cracks in walls or pavements,
etc. In this paper we present trainable B-COSFIRE filters, which are a model of
some neurons in area V1 of the primary visual cortex, and apply it to the
delineation of line patterns in different kinds of images. B-COSFIRE filters
are trainable as their selectivity is determined in an automatic configuration
process given a prototype pattern of interest. They are configurable to detect
any preferred line structure (e.g. segments, corners, cross-overs, etc.), so
usable for automatic data representation learning. We carried out experiments
on two data sets, namely a line-network data set from INRIA and a data set of
retinal fundus images named IOSTAR. The results that we achieved confirm the
robustness of the proposed approach and its effectiveness in the delineation of
line structures in different kinds of images.Comment: International Work Conference on Bioinspired Intelligence, July
10-13, 201
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