Refining Coarse Manual Segmentations with Stable Probability Regions

Most feature-based lesion detection and computer-aided diagnosis methods for medical images require representative data of each region of interest (ROI) for parameter selection. Furthermore, the spatial accuracy of the segmentation of the ROIs from the background can significantly affect certain image features extracted from the ROIs. How- ever, requiring spatially accurate manual segmentations of the ROIs to be used as the ground truth is infeasible for large image sets due to the amount of manual work involved. To relax the requirement of spatial accuracy and to enable spatial refinement of coarse manual segmentations to have more representative feature data, a method based on color information and maximally stable extremal regions of lesion likelihoods is presented. The proposed method is quantitatively compared to several segmentation approaches by using a challenging set of retinal images with spatially accurate ground truth of exudates. The experiments show that the proposed method produces good results measured as Dice coefficients between the refined segmentation and ground truth

Comparison of image registration methods for composing spectral retinal images

Spectral retinal images have signficant potential for improving the early detection and visualization of subtle changes due to eye diseases and many systemic diseases. High resolution in both the spatial and the spectral domain can be achieved by capturing a set of narrowband channel images from which the spectral images are composed. With imaging techniques where the eye movement between the acquisition of the images is unavoidable, image registration is required. In this paper, the applicability of the state-of-the-art image registration methods for the composition of spectral retinal images is studied. The registration methods are quantitatively compared using synthetic channel image data of an eye phantom and semisynthetic set of retinal channel images subjected to known transformations. The experiments show that Generalized dual-bootstrap iterative closest point method outperforms the other evaluated methods in registration accuracy and the number of successful registrations

Evaluation of Publicly Available Blood Vessel Segmentation Methods for Retinal Images

Retinal blood vessel structure is an important indicator of disorders related to diseases, which has motivated the development of various image segmentation methods for the blood vessels. In this study, two supervised and two unsupervised retinal blood vessel segmentation methods are quantitatively compared by using five publicly available databases with the ground truth for the vessels. The parameters of each method were optimized for each database with the motivation to achieve good segmentation performance for the comparison and study the importance of proper selection of parameter values. The results show that parameter optimization does not significantly improve the segmentation performance of the methods when the original data is used. However, the methods’ performance for new data differs significantly. Based on the comparison, Soares method as a supervised approach provided the highest overall accuracy and, thus, the best generalisability. Bankhead and Nguyen methods’ performance were close to each other: Bankhead performed better with ARIADB and STARE, whereas Nguyen was better with DRIVE. Sofka method is available only as an executable and its performance matched the others only with ARIADB

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Towards Phytoplankton Parasite Detection Using Autoencoders

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections