Lung tumor segmentation algorithm

Uzelaltinbulat, Selin; Ugur, Buse

doi:10.1016/j.procs.2017.11.221

Cited by 51 publications

(22 citation statements)

References 7 publications

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“…However, issues like inhomogeneities and small density variation in the CT scans limit the application of these techniques. Uzelaltinbulat and Ugur [15] used thresholding for segmenting the lung scans using the difference between greyscale pixels of lesions and the exterior region. Recently, the application of neural networks in the domain of computer vision has attracted many researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep3DSCan: Deep residual network and morphological descriptor based framework forlung cancer classification and 3D segmentation

Bansal¹,

Chamola

Narang³

et al. 2020

IET Image Processing

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Section: Literature Reviewmentioning

confidence: 99%

Deep3DSCan: Deep residual network and morphological descriptor based framework forlung cancer classification and 3D segmentation

Bansal¹,

Chamola

Narang³

et al. 2020

IET Image Processing

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“…The related research work and findings are presented in this section. Uzelaltinbulat et al [42] presented a lung tumor segmentation technique using Otsu thresholding and morphological operations. Kumar et al [43] proposed a hybrid of the 2D Otsu method and modified artificial bee colony method for the segmentation of the lung CT image.…”

Section: Related Workmentioning

confidence: 99%

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Vijh¹,

Gaurav

Pandey

2020

Neural Comput & Applic

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In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction.

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“…Lung cancer is the type of cancer which unchecks the growth of unusual cells either in one or in both the lungs. These anomalous cells do not perform the functions of healthy human cells and do not mature into normal cells [9]Thresholding is an important technique in image segmentation applications. The basic idea of thresholding is to select an optimal gray-level threshold value for separating objects of interest in an image from the background based on their gray-level distribution [5].Image segmentation needs to segment the object from the background to read the image properly and identify the content of the image carefully, segmentation is necessary to interpretation of an image.…”

Section: Mammogram Images Ct Images Mrimentioning

confidence: 99%

Comparative Study on Cancer Images using Watershed Transformation

Fathin¹,

Nisha²,

Sathik³

2018

IJTSRD

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Digital images are exceptionally huge in the medical image diagnosis frameworks. Image analysis and segmentation are very important tasks in the medical image processing particularly in the field of CAD systems. Visual inspection requires being clear in diagnosis process where the correct region which is affected, need to be separated. Medical imaging plays a very crucial role in all stages of the medical decision process. There are various medical imaging modalities in which mammography are used to detect breast cancer where as MRI for brain tumor and CT for lung cancer. The objective of this paper is to compare the cancer images with different modalities using watershed transformation using metrics.

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Lung tumor segmentation algorithm

Cited by 51 publications

References 7 publications

Deep3DSCan: Deep residual network and morphological descriptor based framework forlung cancer classification and 3D segmentation

Deep3DSCan: Deep residual network and morphological descriptor based framework forlung cancer classification and 3D segmentation

Hybrid bio-inspired algorithm and convolutional neural network for automatic lung tumor detection

Comparative Study on Cancer Images using Watershed Transformation

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