Detection of Macro and Micro Nodule Using Online Region Based-Active Contour Model in Histopathological Liver Cirrhosis

Guptha, Nirmala S; Patil, Kiran Kumari

doi:10.22266/ijies2018.0430.28

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, the researchers implement DNNs in AI analysis techniques for COVID-19 diagnosis. In [ 20 ], a non-overlapping detection algorithm (OR-ACM) was applied for segmenting the normal and abnormal cells. After that, the features are extracted with the aid of HOG and color moments.…”

Section: Introductionmentioning

confidence: 99%

A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images

Tallapragada¹,

Manga²,

Kumar

2023

Multimed Tools Appl

View full text Add to dashboard Cite

Recently, the Covid-19 pandemic has affected several lives of people globally, and there is a need for a massive number of screening tests to diagnose the existence of coronavirus. For the medical specialist, detecting COVID-19 cases is a difficult task. There is a need for fast, cheap and accurate diagnostic tools. The chest X-ray and the computerized tomography (CT) play a significant role in the COVID-19 diagnosis. The advancement of deep learning (DL) approaches helps to introduce a COVID diagnosis system to achieve maximum detection rate with minimum time complexity. This research proposed a discrete wavelet optimized network model for COVID-19 diagnosis and feature extraction to overcome these problems. It consists of three stages pre-processing, feature extraction and classification. The raw images are filtered in the pre-processing phase to eliminate unnecessary noises and improve the image quality using the MMG hybrid filtering technique. The next phase is feature extraction, in this stage, the features are extracted, and the dimensionality of the features is diminished with the aid of a modified discrete wavelet based Mobile Net model. The third stage is the classification here, the convolutional Aquila COVID detection network model is developed to classify normal and COVID-19 positive cases from the collected images of the COVID-CT and chest X-ray dataset. Finally, the performance of the proposed model is compared with some of the existing models in terms of accuracy, specificity, sensitivity, precision, f-score, negative predictive value (NPV) and positive predictive value (PPV), respectively. The proposed model achieves the performance of 99%, 100%, 98.5%, and 99.5% for the CT dataset, and the accomplished accuracy, specificity, sensitivity, and precision values of the proposed model for the X-ray dataset are 98%, 99%, 98% and 97% respectively. In addition, the statistical and cross validation analysis is conducted to validate the effectiveness of the proposed model.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images

Tallapragada¹,

Manga²,

Kumar

2023

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Active Contour can be applied to medical images in very straightforward way, i.e. dental panoramic radiograph [15] and histophatological image [16]. One of the Active Contour with the popular Level Set model is the Chan-Vese Model (CV) [17].…”

Section: Introductionmentioning

confidence: 99%

A New Local Gaussian Variational Level Set for Globus Pallidus Segmentation

Setiawan¹,

Fatichah²,

Sarno³

2020

IJIES

View full text Add to dashboard Cite

Globus Pallidus is an object in Magnetic Resonance Imaging (MRI) with severe intensity inhomogeneity and noises, such as small, low contrast, and weak boundary. Existing active contour methods fall into false boundaries while segmenting objects, which is smaller than the background like Globus Pallidus. This paper proposes a new local gaussian variational level set (NGVLS) for Globus Pallidus segmentation. We developed an energy term to create a smooth curve and prevent evolving curve into the false boundary. In the experiment, we compare NGVLS qualitatively and quantitively with existing methods such as Chan-Vese (CV), Region Scalable Fitting (RSF), Improved Region Scalable Fitting (Im-RSF), Local Pre-Fitting (LPF), and Local Gaussian Distribution Fitting (LGDF). We use Dice Similarity Coefficent (DSC) and Misclassification Error (ME) to measure the accuracy and error of the segmentation, respectively. The experiment is using 40 MRI datasets from Rumah Sakit National Hospital Surabaya, Indonesia. Qualitatively, the results show that NGVLS shows the best segmentation compared to the existing methods. Quantitatively, NGVLS achieves the highest average, minimum, and maximum value from DSC in 0.8291, 0.7119, and 0.9050, respectively. Also, NGVLS achieves the lowest average, minimum, and maximum value from ME in 0.0050, 0.0023, and 0.0155, respectively.

show abstract

Encryption with access policy and cloud data selection for secure and energy-efficient cloud computing

Reddy¹,

Rao

Kumar

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Detection of Macro and Micro Nodule Using Online Region Based-Active Contour Model in Histopathological Liver Cirrhosis

Abstract: Automated image examination of histopathology specimens helps for early detection and better characterization of liver cirrhosis. A new method for the segmentation of overlapping nuclei and non-nuclei cell is evaluated, which is named as Online Region based-Active Contour Model

Cited by 5 publications

References 15 publications

A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images

A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images

A New Local Gaussian Variational Level Set for Globus Pallidus Segmentation

Encryption with access policy and cloud data selection for secure and energy-efficient cloud computing

Contact Info

Product

Resources

About