Wyniki wyszukiwania - Biblioteka Nauki

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

2 Biocybernetics and Biomedical Engineering

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Automated detection of optic disc contours in fundus images using decision tree classifier

100%

Pathan S. , Kumar P. , Pai R. , Bhandary S. V.

Biocybernetics and Biomedical Engineering

2020

tom Vol. 40, no. 1

52--64

Automated segmentation of optic disc in fundus images plays a vital role in computer aided diagnosis (CAD) of eye pathologies. In this paper, a novel method is proposed which detects and excludes the blood vessel for accurate optic disc segmentation. This is achieved in two steps. First, an effective blood vessel detection and exclusion algorithm is developed using directional filter. In the second step, a decision tree classifier is used to obtain an adaptive threshold in order to detect the contour of optic disc. The proposed method aids in computationally robust segmentation of optic disc even in fundus images having illuminations, reflections and exudates. The proposed method is tested on two different datasets which includes 300 fundus images collected from Kasturba Medical College (KMC) Manipal and also the publically available RIM-ONE database. The average values of Jaccard index, dice coefficient, sensitivity, specificity and accuracy obtained for KMC images is 91.28 %, 94.17 %, 92.71 %, 99.89 % and 99.61 % respectively. For RIM-ONE database the obtained average values of Jaccard index, dice coefficient, sensitivity, specificity and accuracy are 85.30 %, 90.69 %, 93.90 %, 99.39 % and 99.15 % respectively. The obtained segmentation results proves the efficiency of the algorithm to be incorporated in CAD of eye diseases.

Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture

100%

Tulsani A. , Kumar P. , Pathan S.

Biocybernetics and Biomedical Engineering

2021

tom Vol. 41, no. 2

819--832

Glaucoma is one of the leading cause of blindness for over 60 million people around the world. Since a cure for glaucoma doesn’t yet exist, early screening and diagnosis become critical for the prevention of the disease. Optic disc and optic cup evaluation are one of the preeminent steps for glaucoma diagnosis. A novel approach is developed in this paper for the identification of glaucoma using a segmentation based approach on the optic disc and optic cup. The Dhristi dataset was used to help improve performance on a small dataset. A custom UNET++ model is built for the segmentation task by tuning the hyperparameters in addition to a custom loss function. The developed loss function helps tackle the class imbalance occurring due to small size of the optic nerve head. The proposed approach achieves 96% accuracy in classifying glaucomatous and non-glaucomatous images based on clinical feature identification. The improvised model is able to achieve state-of-art results for Intersection over Union (IOU) scores, 0.9477 for optic disc and 0.9321 for optic cup, along with providing an enhancement in reducing the training time. The model was tested on publicly available datasets RIM-ONE, DRIONS-DB and ORIGA and is able to achieve an accuracy of 91%, 92% and 90% respectively. The developed approach is validated by training it over RIM-ONE dataset independently, without changing any model parameters. The model provides significant improvement in segmentation of the optic disc and optic cup along with improvement in training time.