Efficient edge detection method for diagnosis of 2D and 3D lung and liver images

Kasturi, M. Mirah; Sutha, P.; Jayanthi, V. E.

doi:10.1109/icisc.2017.8068661

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…For this purpose, the CT data would have to be classified according to the different tissues. Besides the segmentation of skin and bones, analysis of organ segmentation [ 28 , 29 ] as well as an extraction of possible tumors [ 30 , 31 ] have to be implemented. This thermal model could be based on the Pennes bioheat equation in addition to existing models, such as 2D thermal skin models [ 32 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images

Schollemann

Pereira

Rosenhain

et al. 2021

Sensors

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Even though animal trials are a controversial topic, they provide knowledge about diseases and the course of infections in a medical context. To refine the detection of abnormalities that can cause pain and stress to the animal as early as possible, new processes must be developed. Due to its noninvasive nature, thermal imaging is increasingly used for severity assessment in animal-based research. Within a multimodal approach, thermal images combined with anatomical information could be used to simulate the inner temperature profile, thereby allowing the detection of deep-seated infections. This paper presents the generation of anatomical thermal 3D models, forming the underlying multimodal model in this simulation. These models combine anatomical 3D information based on computed tomography (CT) data with a registered thermal shell measured with infrared thermography. The process of generating these models consists of data acquisition (both thermal images and CT), camera calibration, image processing methods, and structure from motion (SfM), among others. Anatomical thermal 3D models were successfully generated using three anesthetized mice. Due to the image processing improvement, the process was also realized for areas with few features, which increases the transferability of the process. The result of this multimodal registration in 3D space can be viewed and analyzed within a visualization tool. Individual CT slices can be analyzed axially, sagittally, and coronally with the corresponding superficial skin temperature distribution. This is an important and successfully implemented milestone on the way to simulating the internal temperature profile. Using this temperature profile, deep-seated infections and inflammation can be detected in order to reduce animal suffering.

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Section: Discussionmentioning

confidence: 99%

An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images

Schollemann

Pereira

Rosenhain

et al. 2021

Sensors

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“…The approach may be found in the present work. Techniques such as image enhancement and Sobel edge segmentation are used in the process of diagnosing cancer in its early stages [16].…”

Section: Reviews On Lung Image Classification Systemsmentioning

confidence: 99%

CT Lung Image Classification Systems - A Systematic Review

Prabhakar¹,

Sivasubramanian²,

Sundaram³

et al. 2023

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Computer Aided Detection (CAD) and diagnostic technologies and their application have emerged in the last decade. CAD applications have concentrated on identification of the region of interest, for example nodules in the lung in the images, and analysis of the characteristics of objects. CAD technologies take advantages of computer systems as well as image processing and analysis techniques to overcome perceptual and interpretive errors caused by radiologists in the process of image observation and interpretation. This fact is inevitable in the context of medical practice since experience of image readers may vary and each observer (such as a radiologist) has a unique visual search on an image. Furthermore, there are always inter and intra observer variability for interpretation of an image. To overcome these difficulties, different CAD systems are developed for medical image analysis. This paper presents a systematic review on different CAD systems for lung cancer classification using computed tomography images.

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“…The disadvantage of this strategy is yield relies upon the window size, and the calculation is costly. (Kasturi et al, 2017) proposed an edge detection technique to segment lung cancer in 2D and 3D lung scans. (Huidrom et al, 2017) proposed an approach of thresholding segmentation of lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

Umamaheswari

Bakiyalakshmi

Joshi

et al. 2020

ijrps

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Treating malignant growth in the beginning times gives greater treatment choices, less intrusive medical procedure and expands the endurance rate. Finding lung disease just as liver malignancy at a beginning period is a difficult assignment since there are hardly any manifestations right now larger part of the cases are analyzed in later stages. In this paper, an augmented method of segmentation is proposed using the Fuzzy K-means algorithm to observe the initial stage of lung cancer from the human chest X-Ray images. Initially, the investigator used strategies like Histogram Equalization for image improvement, Watershed technique for segmentation and Edge detection for Extraction, etc. Existing Approaches decline to provide certainty in a problem-solving time operation. The datasets are collected from LIDC-IDRI and Kaggle. In this, 100 training dataset input images are taken for training the model: 80 Cancerous images and 20 non-cancerous images. To evaluate the data 20 images are taken. The detection means classifying tumour into three classes (i) non-cancerous tumour, (ii) Less affected tumour (iii) Highly affected tumour. Hence, a new technique to detect lung carcinoma nodules using median filters, Fuzzy K-means clustering for segmentation and SVM (Support Vector Machine) as the classifier is planned on this work. Hence, this way is profitable to the medical instruments producing industries and additionally guide the radiologist for the early analysis of cancer.

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Efficient edge detection method for diagnosis of 2D and 3D lung and liver images

Cited by 7 publications

References 4 publications

An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images

An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images

CT Lung Image Classification Systems - A Systematic Review

Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

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