Detection of retinal hemorrhage in color fundus image using splat feature segmentation

G, Arun; Sasirekha, N.

doi:10.1109/iciiecs.2015.7192928

Cited by 6 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AC determines how accurately a classifier predicts both the negative and the positive classes. (N. Sasirekha et al, 2015).…”

Section: Simulation Results and Comparison For Rf Cnn And Annmentioning

confidence: 99%

Automatic liver tumor segmentation from CT images using random forest algorithm

Sasirekha,

Anitha,

et al. 2023

The Scientific Temper

View full text Add to dashboard Cite

Automatic liver segmentation is challenging, and the tumor segmenting process adds more complexity. Based on the grey levels and shape, separating the liver and tumor from abdominal CT images is critical. In our paper suggests a more effective approach by using Gabor features (GF) to segment liver tumors from CT images and three alternative neural network algorithms to address these problems: RF, CNN and ANN. This thesis uses the same collection of classifiers and GF to first segment a variety of Gabor liver images. The organ (liver) is then extracted from an abdominal CT image using liver segmentation, which is done by three classifiers: ANN, CNN, RF trained on Gabor filter and the tumor segmentation is done by the human visual system (HVS). For pixel-wise segmentation, reliable and accurate ML techniques were used. For the liver segmentation, the classification accuracy was 99.55, 97.88 and 98.13% for RF, CNN and ANN, respectively. From the extracted image of liver, the classification accuracy for tumor was 99.52, 98.07 and 98.45% for RF, CNN and ANN, respectively.

show abstract

“…AC determines how accurately a classifier predicts both the negative and the positive classes. (N. Sasirekha et al, 2015).…”

Section: Simulation Results and Comparison For Rf Cnn And Annmentioning

confidence: 99%

Automatic liver tumor segmentation from CT images using random forest algorithm

Sasirekha,

Anitha,

et al. 2023

The Scientific Temper

View full text Add to dashboard Cite

show abstract

“…The detection of HM patterns that are very close to the vascular structure is observed to be a time consuming and difficult task for clinical practitioners. To deal with this issue, rule-based mask detection [86], splat-based segmentation algorithms [87][88][89], and basic knowledge of inter-/intra-retinal structures [90] have produced impressive quantitative results. In a previous study [86], an image normalization step was applied to remove the normal features, i.e., blood vessels, disc, and fovea, and to improve the contrast of an input image.…”

Section: Dr-related Lesion Detection Methodsmentioning

confidence: 99%

“…To localize the HM region, three Gaussian templates were employed, yielding an SE of 93.3% and an SP of 88%. The authors in [87][88][89] considered splat-based HM segmentation methods with different configurations. Despite the better visual HM detection results, they did not present numerical measures when using a large number of tested images.…”

Section: Dr-related Lesion Detection Methodsmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

show abstract

“…Threshold segmentation method is the most direct and the most practical method, which makes it to get widely used segmentation algorithm in the image segmentation algorithm, and by reference to the fields. Threshold segmentation method is to use the threshold parameters for image segmentation, these thresholds according to characters of the image characteristics of the different participants with gray threshold, texture feature threshold, the threshold selection of naturally becomes the key to the threshold segmentation method [11][12]. (2) The segmentation technology based on the regional characteristics.…”

Section: Introductionmentioning

confidence: 99%