A novel approach of lung segmentation on chest CT images using graph cuts

Dai, Shuangfeng; Lü, Ke; Dong, Jiyang; Zhang, Yifei; Chen, Yong

doi:10.1016/j.neucom.2015.05.044

Cited by 97 publications

(50 citation statements)

References 21 publications

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“…It can be seen from Figure 9 that the segmented lung images are very similar, and the difference between the segmented lung image with our method and that with the graph-cut based method presented in [5] and also that with the method proposed in [11] are too tiny to observe.…”

Section: Discussionmentioning

confidence: 86%

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

Shi

Zhao

et al. 2016

BioMed Research International

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Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

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Section: Discussionmentioning

confidence: 86%

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

Shi

Zhao

et al. 2016

BioMed Research International

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“…Using more features of images, like color values of pixels for color images, provides better results in terms of accuracy than using gray-level values. In the RGB color spaces, each pixel in a color image has three-color values as red, green, and blue values (Peng et al 2013;Cheng et al 2001;Dai et al 2015;Mignotte 2008). Therefore, the cluster centers have a vector that consists of three values instead of one value.…”

Section: Na Baykan a Saglammentioning

confidence: 99%

Fast K-Means Color Image Clustering With Normalized Distance Values

Baykan

Sağlam

2018

Scitech

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ABSTRACT:Image segmentation is an intermediate image processing stage in which the pixels of the image are grouped into clusters such that the data resulted from this stage is more meaningful for the next stage. Many clustering methods are used widely to segment the images. For this purpose, most clustering methods use the features of the image pixels. While some clustering method consider the local features of images by taking into account the neighborhood system of the pixels, some consider the global features of images. The algorithm of the K-means clustering method, that is easy to understand and simple to put into practice, performs by considering the global features of the entire image. In this algorithm, the number of cluster is given by users initially as an input value. For the segmentation process, if the distribution of the pixels over a histogram is used, the algorithm runs faster. The values in the histogram must be discrete in a certain range. In this paper, we use the Euclidean distance between the color values of the pixels and the mean color values of the entire image for taking advantage of the every color values of the pixels. To obtain a histogram that consists of discrete values, we normalize the distance value in a specific range and round the values to the nearest integers for discretization. We tested the versions of K-means with the gray-level histogram and the distance value histogram on an urban image dataset getting from ISPRS WG III/4 2D Semantic Labeling dataset. Comparing the two histograms, the distance value histogram proposed in this paper is better than the gray-level histogram. Key Words: Clustering, Histogram, Image segmentation, K-meansNormalize Edilmiş Uzaklık Değerleri ile Hızlı K-ortalama Renkli Görüntü Segmentasyonu ÖZ: Görüntü segmentasyonu, görüntü piksellerinin kümelere gruplandığı orta seviye bir görüntü işleme aşamasıdır. Öyle ki, bu şamadan elde edilen veri daha sonraki aşamalar için önceki veriye göre daha anlamlı hale gelmiş olur. Birçok kümeleme metodu, görüntü segmentasyonu amacıyla yaygın bir şekilde kullanılmaktadır. Bu amaçla, çoğu kümeleme metodu görüntü piksellerinin özelliklerini kullanmaktadır. Bazı kümeleme metotları piksellerin komşuluk sistemini kullanarak görüntünün yerel özelliklerini ele alırken, bazıları da görüntünün genel özelliklerini ele almaktadır. Anlaşılması kolay ve uygulaması basit olan K-ortalama algoritması, bütün görüntünün özelliklerini ele alarak segmentasyon yapmaktadır. Bu algoritmada, küme sayısı başlangıç giriş değeri olarak kullanıcı tarafından verilmektedir. Bu segmentasyon işlemi için, eğer piksellerin bir histogram üzerindeki dağılımı kullanılırsa algoritma daha hızlı çalışmaktadır. Bu histogram üzerindeki değerler belirli bir aralıkta ve ayrık olmak zorundadır. Bu çalışmada, piksellerin her bir renk değerinden faydalanmak için piksellerin renk değerleri ile görüntünün ortalama renk değerleri arasındaki Öklit uzaklığı kullanılmıştır. Ayrık değerlerden oluşan bir histogram elde etmek için, uzaklık değerlerini belirli değer ara...

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“…A mean IoU of 95.81% ± 0.89% was achieved, also stated that every juxtapleural nodules was included in the segmentation. Another graph cut segmentation approach is the one developed in [9], which uses Gaussian mixture models (GMMs) and expectation maximization (EM). After a noise-removal Gaussian smoothing, a graph representing the image is constructed, then the min-cut/max-flow algorithm is applied to segment it into foreground and background.…”

Section: A Related Workmentioning

confidence: 99%

Extracting Lungs from CT Images Using Fully Convolutional Networks

Alves

Neto

Oliveira

2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Analysis of cancer and other pathological diseases, like the interstitial lung diseases (ILDs), is usually possible through Computed Tomography (CT) scans. To aid this, a preprocessing step of segmentation is performed to reduce the area to be analyzed, segmenting the lungs and removing unimportant regions. Generally, complex methods are developed to extract the lung region, also using hand-made feature extractors to enhance segmentation. With the popularity of deep learning techniques and its automated feature learning, we propose a lung segmentation approach using fully convolutional networks (FCNs) combined with fully connected conditional random fields (CRF), employed in many state-of-the-art segmentation works. Aiming to develop a generalized approach, the publicly available datasets from University Hospitals of Geneva (HUG) and VESSEL12 challenge were studied, including many healthy and pathological CT scans for evaluation. Experiments using the dataset individually, its trained model on the other dataset and a combination of both datasets were employed. Dice scores of 98.67% ± 0.94% for the HUG-ILD dataset and 99.19% ± 0.37% for the VESSEL12 dataset were achieved, outperforming works in the former and obtaining similar state-of-the-art results in the latter dataset, showing the capability in using deep learning approaches.

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A novel approach of lung segmentation on chest CT images using graph cuts

Cited by 97 publications

References 21 publications

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images

Fast K-Means Color Image Clustering With Normalized Distance Values

Extracting Lungs from CT Images Using Fully Convolutional Networks

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