59 research outputs found
Retinal image segmentation and quantification of vessel width in non-standard retinal datasets
Get PDFThe human retina has the potential to reveal important information about retinal, ophthalmic, and even systemic diseases such as diabetes, hypertension, and arteriosclerosis. Automatic quantification of retinal vessel morphology and width is considered as a first step in computer assisted medical applications related to diagnosis and treatment planning. This work aims to quantify the blood vessels in noisy and pathological retinal images of school children with uneven illumination and containing complex vessel profiles. In this thesis, we have presented two methodologies of retinal vessel segmentation and an algorithm for vessel width measurement. The unsupervised method of retinal segmentation is based on detection of vessel centrelines and followed by computing the vessel shape and the orientation map using morphological bitplane slicing. A supervised method for segmentation of blood vessels by using an ensemble classifier of boosted and bagged decision trees is also presented. The feature vector encodes information to successfully handle both normal and pathological retinas with bright and dark lesions simultaneously. The obtained performance metrics illustrate that this method outperforms most of the state-of-the-art methodologies of retinal vessel segmentation. The method is computationally fast in training and classification and needs fewer samples for training than other supervised methods. It is training set robust as it offers a better performance even when it is trained and tested on different sets of retinal images. A new public database of the retinal images taken from multi-ethnic school children is presented along with the ground truths of vessel segmentation and width measurement. We have also introduced a robust and accurate methodology for measuring the calibre of vessel segments in retinal images of multi-ethnic children. The vessel centrelines are detected from the vessel probability map image resulting from ensemble classification. The vessel branch points and crossovers are identified and removed from the vessel centreline image to obtain vessel segments followed by computing the local vessel orientation of the vessel segments. The width of each vessel segment is estimated using a two dimensional model with incorporated Gaussian (for ordinary vessels) as well as Difference of Gaussian profiles (for vessels with a central reflex). The automated methods for quantification of retinal vessel morphology and width may be used as an alternative to the time consuming subjective clinical evaluation for monitoring the progression of retinopathies and their association with normal and abnormal vascular patterns. This may enable a quick diagnosis, treatment availability, prognosis, and facilitation of clinical heath-care procedures in remote areas
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
Get PDFMedical image segmentation assists in computer-aided diagnosis, surgeries,
and treatment. Digitize tissue slide images are used to analyze and segment
glands, nuclei, and other biomarkers which are further used in computer-aided
medical applications. To this end, many researchers developed different neural
networks to perform segmentation on histological images, mostly these networks
are based on encoder-decoder architecture and also utilize complex attention
modules or transformers. However, these networks are less accurate to capture
relevant local and global features with accurate boundary detection at multiple
scales, therefore, we proposed an Encoder-Decoder Network, Quick Attention
Module and a Multi Loss Function (combination of Binary Cross Entropy (BCE)
Loss, Focal Loss & Dice Loss). We evaluate the generalization capability of our
proposed network on two publicly available datasets for medical image
segmentation MoNuSeg and GlaS and outperform the state-of-the-art networks with
1.99% improvement on the MoNuSeg dataset and 7.15% improvement on the GlaS
dataset. Implementation Code is available at this link: https://bit.ly/HistoSegComment: Accepted by 2022 12th International Conference on Pattern Recognition
Systems (ICPRS), For Implementation Code see https://bit.ly/HistoSe
NLP Meets Vision for Visual Interpretation - A Retrospective Insight and Future directions
No full textDCARN: Deep Context Aware Recurrent Neural Network for Semantic Segmentation of Large Scale Unstructured 3D Point Cloud
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Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm
Get PDFAutomated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc
Ensemble classification applied to retinal blood vessel segmentation: theory and implementation
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