A Comparative Analysis of Image Segmentation Techniques Toward Automatic Risk Prediction of Solitary Pulmonary Nodules

Mukherjee, Jhilam; Shaikh, Soharab Hossain; Kar, Madhuchanda; Chakrabarti, Amlan

doi:10.1007/978-81-322-2650-5_11

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Deep learning, Support Vector Machine, Convolutional Neural Network (CNN) Bora, Kangkana, et al [6] For the purpose of obtaining shape features, a brand-new segmentation method has also been put out in this study. Color and texture features have been analysed using the Ripplet Type I transform, Histogram first order statistics, and Gray Level Co-occurrence Matrix, respectively.…”

Section: Histogram Of Local Binarymentioning

confidence: 99%

Image processing techniques for the segmentation of cervical cancer

Poornappriya

Gopinath

2022

ijhs

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The most prevalent type of cancer in women worldwide is uterine cervical cancer. The majority of cervical cancer (CC) cases can be avoided by participating in screening programmes that look for precancerous lesions. Colposcopic cervigrams or images from digital colposcopy have been acquired in raw form. This study presents a novel framework that combines image enhancement, pre-processing, and image segmentation to identify cervical cancer. Three phases make up this framework: the Dual Tree Discrete Wavelet Transform (DTDWT) for pre-processing, the Curvelet transform and Contour Transform (CC) for image improvement, and the K-means clustering for segmentation.

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Section: Histogram Of Local Binarymentioning

confidence: 99%

Image processing techniques for the segmentation of cervical cancer

Poornappriya

Gopinath

2022

ijhs

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“…Cervix is linear part of lower uterus because of that it is defined as a neck of uterus. In USA, cancerous community described that they found of 12,800 new cases with cervical cancer for every year and it leads to 4000 deaths, and also stated that cervical cancer is the highest reason of death among women [1]. It is also evident that cervical cancer lies in the age below 50.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Detection of Cervical Cancer

Pushpalatha

2018

AJEAT

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Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for segmentation (iv) features computation using Gray level co-occurrence matrix (v) classification using adaptive Support vector machine. The experimental results evident that proposed technique is superior to existing methodologies.

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“…Cervix, a part connecting the uterus with vagina called as womb. In general, cervical cancer is categorized into adenocarcinoma and squamous carcinoma, and squamous cell carcinoma is the most common one and is the fourth most common type of cancer among women [1,2], and is found in the mid age of women. Though it is preventable, it leads to death in the advance stage.…”

Section: Introductionmentioning

confidence: 99%

Cervical Cancer Detection: A Literature Survey

Pushpalatha,

Sathiamoorthy,

M. Kamarasan

2018

AJES

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Cervical cancer is more common in women and worldwide it is most feared disease. Due to abnormal growth in the cervix cells, cervical cancer occurs and slowly it also spreads to the other organs of human body. Cervical cancer is caused by number reasons like human papilloma virus, using birth control pills, cigarette smoking, etc. In the initial stage, cervical cancer will not show any signs. However, if it is identified in earlier stage, it will be cured successfully. Nowadays, number of computer vision based approaches has been introduced to identify the cervical cancer disease and its stages. Still more research in this domain is ongoing towards getting high accuracy in the disease and stage prediction. In this paper, we studied a detailed literature on recognition of cervical cancer in connection with computer vision approaches.

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A Comparative Analysis of Image Segmentation Techniques Toward Automatic Risk Prediction of Solitary Pulmonary Nodules

Cited by 3 publications

References 17 publications

Image processing techniques for the segmentation of cervical cancer

Image processing techniques for the segmentation of cervical cancer

A Novel Framework for Detection of Cervical Cancer

Cervical Cancer Detection: A Literature Survey

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