Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

IET Image ProcessingVolume 14, Issue 14 p. 3355-3365 Research ArticleFree Access Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques K. Vijila Rani, Corresponding Author kumar1966.t@gmail.com ECE, Arunachala College of Engineering for Women, Nagercoil, TN, 629203 IndiaSearch for more papers by this authorS. Joseph Jawhar, EEE, Arunachala College of Engineering for Women, Nagercoil, TN, 629203 IndiaSearch for more papers by this author K. Vijila Rani, Corresponding Author kumar1966.t@gmail.com ECE, Arunachala College of Engineering for Women, Nagercoil, TN, 629203 IndiaSearch for more papers by this authorS. Joseph Jawhar, EEE, Arunachala College of Engineering for Women, Nagercoil, TN, 629203 IndiaSearch for more papers by this author First published: 21 October 2020 https://doi.org/10.1049/iet-ipr.2020.0407AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract A chronic disorder caused by abnormal growth of the lung cells in the pulmonary tumour. This study suggests a modern automated approach to improve efficiency and decrease the difficulty of lung tumour diagnosis. The proposed algorithm for lung tumour sensing consists of four phases: pre-processing, segmentation, extraction, and characteristics classification. The first stage is the image acquisition here input lung image is read and then resized. The second stage is the image pre-processing here Perona–Malik diffusion with unsharp masking filter is proposed for enhancement purposes. The third stage is the segmentation here the improved histogram–based fast 2D Otsu's thresholding is proposed for lung tumour segmentation purposes. Finally, linear discriminant analysis classifier, support vector machine (SVM) classifier, SVM–sequence minimal optimisation classifier, Naive Bayes classifier, SVM–advance sequence minimal optimisation (SVM–ASMO) classification [proposed] included in the various classifier groups adopted in this report. Overall performance accuracy of 0.962 is obtained using the proposed SVM–ASMO method that helps to diagnose the cancer cells using the feature extraction process, which is done automatically. The specificity, precision, recall, and F1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984,respectively. 1 Introduction Medical image analysis is currently a rigorous field of study that requires considerable changes. Medical image processing has greatly helped millions of people around the world in their treatment and therapy. Computed aided diagnosis (CAD) systems are extremely important in clinical diagnosis for their robustness, flexibility, and accuracy. CAD devices may be used to analyse computed tomography (CT) images with high resolution and low resolution. During the last two decades, the rapid development of CAD systems was visualised. Quantitative lung image processing with a naked eye is conducted by radiologists. The human eye is not able to analyse the grey lung picture minute lesion information because the tumour boundary must be wrongly identified and a single lesion misclassified and the lung lesion must be successfully detected. The segmentation algorithms should have faster convergence for real-time applications. Many steps have been taken to examine medical pulmonary images. The identification of lung tumours is a difficult task in medical imaging and a great deal of work is ongoing in this regard. The identification of patterns is called a highly complex method for making meaningful decisions based on derived functional vectors. The irregular splitting of lung cell allows lung cancer to operate incorrectly. More than 100 lung cancer forms have been discovered at present. When the tumour is identified in the early stages, the survival rate is high. Tumour identification is a very complex process because overlapping lung tissues cover tumours. Most lung cancer signs (bad appetite, weight loss, fever, tiredness) are not specific. Automatic lung image processing involves software based on strong pre-processing, segmentation, extraction, and classification algorithm. The design and viability of the algorithms is a major factor in the smooth functioning of the CAD systems. Many approaches for measuring diagnostic efficiency were used for each of these processes. Manian et al. [1] proposed logical texture classification operators in their research. They have built logical operators and extracted features with the logical operators for function extraction. Functional selection is then made by the distance and design. We have graded according to Bayes, Euclidean, Mahalanobis, K-nearest neighbour (KNN), and left one classifier out. They have reached the right percentage classification of 78% in the Bayes category, 96% in the Euclidean, 85% in the Mahalanobis category, 94% in the KNN, and 94% in the left one out category. CT images are preferable to show low contrast or small volume nodules by decreasing the thickness of the slice to build a CAD for a device for lung tumour detection. The main contributions of this paper are given in detail. Obtain the CT image from the Lung Image Database Consortium (LIDC) database [online] and in-house clinical (private) International Cancer Center Neyyoor (ICCN) at Tamil Nadu database. Initially, it undergoes pre-processing using Perona–Malik diffusion with unsharp masking filter, to reduce noise [proposed]. Then segmentation is done by improved histogram-based fast 2D Otsu's thresholding (IHBF2DOT) [proposed]. Segmentation accuracy calculation is to be done through various performance metrics. In addition to this, comparison exists with past works. Finally, the classification based on linear discriminant analysis (LDA) classifier, support vector machine (SVM) classifier, SVM–sequence minimal optimisation (SVM–SMO) classifier, Naive Bayes classifier, [proposed] SVM–advance sequence minimal optimisation (SVM–ASMO) classification are analysis. In the diagnosis of lung cancer, these previous different classification methods were used. The characteristics, advantages, and disadvantages of each approach are unique. Due to identifying cancer in its early stage is of great significance, a significant number of studies are still being carried out. The remaining part of this paper is organised as follows. The related work is explained in Section 2. Section 3 briefly reviews the main block of the proposed method. The experimental results and performance evaluation are shown in Section 4. Finally, a conclusion is given in Section 6. 2 Related work Henschke et al. [2] developed their CAD system that will automatically detect and extract features along with it to classify the nodules. The neural network's clustering technique is used for extracting and grouping the features into clusters. In their classification of 28 benign and malignant nodules, only three benign nodules were misclassified. Erberich et al. [3] applied the Hough transform (HT) for both 2D circles and 3D spheres using a rule-based classifier and achieved 30–40% per-nodule sensitivity with a 'large amount of false-positive nodules'. The automated method for identifying lungs in 3D CT images by a three-step framework was proposed by [4]. Next, the lungs were removed from a grey level threshold. Afterwards, the lung border was identified using dynamically programmed operations and, eventually, the lung border was applied with a series of morphological operations. The mean average difference of 0.58 pixels between the manually segmented lung and pulmonary segmentation according to the proposed method, was found the value of exact segmentation and as a pre-processing scheme in the CAD system. Their experience in processing images for the identification of pulmonary nodules indicates that the lack of information varied between 5 and 17% due to the segmentation of pre-processing. Besides, an algorithm used to segment it was designed for the specific detection of nodular activity. It was also developed. The research on the computerised analysis of CT lung images was given by Sluimer et al. [5]. Yoo et al. [6] adopted a multi-phase level set framework for segmenting partly solid nodules. The mean segmentation error is 0.96%, but the median percentage volume error is as high as 40%. The major drawback of this method is that the initial point selection is highly sensitive and failure to adapt the boundaries because of the inhomogeneities in the lung region. The issues of segments and lung structures are outlined and the description and quantification of lung disorders are answered. To calculate the threshold value by reducing the difference between the forward projection of the selected image and the data on determining projection, Batenburg and Sijbers [7] used a projection minimisation approach using tomographic projection data. The probability density function (PDF) is used to represent effective lung districts from the high-resolution CT scan with a new optimal threshold algorithm definition in lung image segmentation. Suarez-Cuenca et al. [8] demonstrated an automated detection of pulmonary nodules in CT for the FP reduction by combining multiple classifiers. Experimented classifiers are LDA, quadratic discriminant analysis (QDA), ANN and SVM. They applied independently and combined to the LIDC using 85 images which have 110 cancerous nodules. The reported sensitivity is 80%, and the number of FPs/patients for each of the six classifiers is 6.1 for LDA, 19.9 for QDA, 8.6 for ANN, and 17.0 for SVM. Elizabeth et al. [9] proposed an automated chest parenchyma segmentation system in their study. Within two steps, you did it. The lungs were first segmented to optimal levels and the pulmonary borders rebuilt with the lung region's property, convex hull, and centroid. They tested the method using it into a CAD program for lung cancer diagnostics and achieved 97% accuracy compared with 88.5% achieved with optimal threshold segmentation. Gan et al. [10] suggested the new tumour classification sparse representation (SR) method using gene expression data. The proposed approach is used first to remove noise from the original sample data, by using a latent low-rank representation for extracting salient features. Then we use an SR classification (SRC) to construct tumour classification models obtain an accuracy of 95.07%. The CAD system was found to detect large peripheral using the proposed segmentation approach, which could not be detected with CAD systems using optimum threshold-based separation and still unsuitable in the presence of very large peripheral benzodiazepine receptors. Kuruvilla and Gunavathi [11] described a CAD system for the detection of lung cancer using ANN. The classifications were done by feedforward and feedforward-back propagation networks. Their study concluded that the feedforward-back propagation network provided better classification results. The implemented ANN yielded a sensitivity of 91%, the specificity of 100%, and an accuracy of 93%. Song et al. [12] suggested an automated approach to the segmentation of lung lesions based on them through the algorithm of the slide and backpropagation. There are no training datasets available in this process and the result gives high sensitivity to detection with few false positives. Few lesions offer a 60% precision uncontrolled process. John and Mini [13] used multiple levels of thresholding to segment the lung from other parts of the CT image. In their studies, they included irregular lung walls. Their work was well suited for CT images with solitary nodules. The major drawback of this thresholding method is that the accuracy depends on the quality of the image, which is determined by the acquisition method and scanner type. Also, the densities of the different parts are similar and hard to differentiate. Zhang et al. [14] developed a progressive lung clustering image sequence segmentation approach based on superpixels and the spatial clustering algorithm based on density. The findings of mean square error (MSE) and volume of interest (VOI) values 0.0736 and 2.2933 are evaluated in detail, MSE, and voxel index values. Global tissue property figures have been estimated by Hsieh et al. [15] using greyscale histogram moments. Quantifying histogram moments help to recognise the shape characteristics. Such earlier works suggested that the extraction of functionality is an important step in the lung analysis and defines the accuracy of the proposed classification by the efficiency of the extracted products. The likelihood that an error occurs can be minimised and the classifier successfully implemented if optimally extracted feature vectors are achieved. Siva Kumar and Chandrasekar [16] suggested an efficient lung nodule detection system for CT images. The weighted fuzzy clustering possibility approach allows for the segmentation of nodule images. They showed that the SVM classifier of the RBF kernel surpassed the SVM classifier of the linear and polynomial kernel. The sensitivity and precision obtained in detection were 82.05 and 80.36%. The study of lung cancer in microscopic images with the most detailed learning technique called the deep-convolutional neural network (DCNN) classification in [17], has shown that 70% of the images were correctly categorised. The literature provides several research papers with various approaches to each method. One main challenge is the development of a CAD system that works on every case independently of the data base's modality, quality, and quantity. Vijila Rani and Joseph Jawhar [18] provided a comprehensive and thorough study of various methods used in CAD systems. Parallels and the combination of several algorithms will improve the efficiency of the segmentation of the pulmonary image according to research in today's biological environment. On the other hand, based on the analysis, we can establish a robust segmentation system that can efficiently detect and identify lung tumour boundary areas. Although the conventional automated system predicts lung cancer successfully, it continues to maintain its accuracy of recognition [19] and also takes longer to handle large volumes of data. The successful method for the identification of lung cancer, which has been discovered by Singh and Gupta [20], classifies photos of lung cancer as benign and malicious. The various computer classifications include Naïve Bay classifier, decision trees classifier, descent classifier, k-closest neighbour classifier, vector support classifier for supervised learning, random forest classifier, and neural network classification for lung cancer scoring. In contrast with the other six classification classifiers, the performance of the multi-layer perceptron classifier reaches 88.55%. Also, it cannot process minimum CT images, which can lead to false lung characteristics that create further misclassification rates [21]. Thanks to their ideas for creating an intellectual cancer prediction method, the individual authors submitted their opinions about the process of lung cancer identification. Vijila Rani and Joseph Jawhar [22] Suggested superpixel with nanoscale imaging and boosted DCNN (BDCNN) concept for lung tumour classification. The classification of the image can be done by using BDCNN concept yields an accuracy of 97.3%. Shi et al. [23] proposed multi-modal sparse representation-based classification (mSRC), for classifying lung needle biopsy images. The proposed mSRC can achieve significant improvement (mean accuracy of 88:1%, precision of 85:2%, recall of 92:8%), especially for classifying different cancerous types from 271 images. Research gap identified and the justification of the proposed method: The segmentation in the existing methods is not accurate and tumour boundary identification is difficult where there is an overlapping of the cells in the images. More time consuming for larger data sets due to the non-linear segmentation of the tumour pixel selection and also due to the improper boundary detection. We are proposing a new approach, which proves successful detection and takes comparatively less time, in comparison to the other conventional system. 3 Proposed method The main block of the proposed method is shown in Fig. 1. This proposal focuses on the accurate detection of lung tumour boundary on CT images. However, validation of the proposed work is done on public LIDC database and in-house clinical database is used for the research process. The publically available LIDC CT scan of 455 patients containing 410 nodules was tested and in-house clinical dataset of 80 patients with 290 CT exams is obtained from ICCN in Tamil Nadu, India. A feature vector is produced from the segmented results of the image. From the fully automatic process, extract shape features, intensity, texture features, and classify it as healthy or not. Fig. 1Open in figure viewerPowerPoint Basic block diagram of proposed lung tumour classification model 3.1 Image enhancement and segmentation Medical images carry very important information about our body organs and structures. The acquired tools used to collect the medical images usually produce some noises depending on the imaging modality. Image enhancement and denoising is needed for the diagnosis of patients. The algorithm that will remove the noise and enhance the contrast of the medical image is very important. The goal of image quality assessment is to measure the strength of the perceptual similarity between denoised images and reference images. The existing pre-processing techniques like median filter, Gaussian filter and Gabor filter applied on lung CT images result in distorted boundaries, distorted edges and time-consuming process whereas Perona–Malik diffusion filtering enhances the brightness of the image. Perona–Malik diffusion with unsharp masking filter (PMDWUM filter) is used by the proposed system to improve the quality and sharpens the edges of the images. First, the filter smoothens the abrupt variations in the homogeneous regions while preserving the strong edges. The smoothening is mathematically expressed to be a diffusion process where the diffusion strength is controlled by local gradients. The process of diffusion is expressed as follows: (1)where represents the intensity at the spatial coordinate vector, at an iteration step t. The diffusion process is controlled by the coefficient, , which is a function of the magnitude of intensity gradient, . The diffusion coefficient is conventionally defined to provide higher rates of diffusion in homogeneous regions and at discontinuities, where the gradient is high; diffusion is reduced in order to preserve the edges. The widely used functions for diffusion coefficients are defined as (2) (3)where the parameter is selected based on the noise level and the gradient strength. In the discrete implementation, the local gradients are found by difference operation between neighbouring elements. The diffusion in 2D implementation is formulated as below: (4) The flow function is the product of diffusion coefficient and the gradient along with the direction i of the 3 × 3 region under consideration. symbolise east, west, south and north directions, respectively. The filter updates the centre pixel based on the flow from its eight immediate neighbour pixels, as shown in Fig. 2. are set as 1 and the distance to diagonal neighbours is longer, and is taken as . The 2D local neighbourhood region can be considered as an image and the centre pixel is updated with the flow contributions from the eight-way connected as (5) (6)This filter especially helps in sharpening the edges of the tumour region in the CT images. Fig. 2Open in figure viewerPowerPoint Flow of updating the centre pixel Segmentation: In this research paper, IHBF2DOT is proposed for segmentation purpose. S: I→R is the process of subdividing or partitioning of the image plane into a set of disjoint homogeneous regions , where i = 1, 2, …., m and R is connected such that the following are true: (7)The objective of segmentation is to divide the image plane into the region of interest (ROI) and the background. It is a useful midlevel representation of an image to distinguish the ROI from the background. The IHBF2DOT considers the spatial correlation of a pixel within its neighbourhood in addition to the pixel intensity in order to segment the image into an object and the background. Let the image under consideration be represented by L grey levels. Then the average pixel intensity within the neighbourhood of any pixel in the image is defined as follows: (8)where is the half of the neighbourhood size. The average intensities are also represented with L grey levels. A pair is formed at every pixel with actual intensity and the corresponding the average intensity and the 2D histogram of the image represents the joint probability of occurrence of such pairs which is given by (9)where N represents the total number of pixels in the image and denotes the frequency of occurrence of a pair, . Also, the following equations hold true: (10) (11)In the majority of the practical scenarios, the probability of distant diagonal components in the 2D histogram is nearly zero and if the threshold for clustering is located at the pair , then let the two clusters nearer to the diagonal separated by this threshold be and . Then the probabilities of the two clusters, and are defined by (12) (13)The mean intensity vectors of the two clusters are given by (14) (15)and the total mean vector of the 2D histogram is estimated as follows: (16)In the majority of practical scenarios, the distant diagonal classes have negligible probabilities and therefore, the following equation can be assumed to be true: (17)From the above equation, the total mean vector is found as follows: (18)The optimum threshold pair is assumed to maximise the inter-class scatter which is defined as (19)The measure for intra-class scatter has been chosen as the trace of this matrix which is given in the following equation: (20)The optimum threshold pair maximises the inter-class scatter (21)Here, since, the search space for optimal threshold pair is 2D, a higher computational complexity is expected. The length of the diagonal is and let c be the discrete entries constitute the diagonal. The diagonal is discretised such that c takes values that satisfy . The joint probability at any point in the 2D histogram space, for instance to be projected to the diagonal as follows: (22)The projected values on the diagonal are considered as another histogram with levels. The 1D Otsu's thresholding technique is utilised on this projected diagonal value to obtain the optimum threshold pair. Let O be the global minimum threshold obtained from the 1D Otsu's thresholding on the diagonal. Then a line, vertical to the diagonal is formulated that pass through the point, O. The optimum threshold pair, lies on the line that is formulated as follows: (23)The above line intersects the X-axis and Y-axis at and . The searching space in the 2D histogram is reduced by searching from 0 to and that satisfy . The computational time is further reduced by making use of look-up tables that are formulated by traversing the 2D histogram initially for one time (24) (25) (26) (27) (28)Let (29) (30) (31) (32) (33) (34)The vectors necessary for the trace calculations are made into a look-up table, as shown in the above equations. The same is utilised while estimating the trace avoiding the redundant calculations. In this paper, we proposed an improved image segmentation method to overcome the shortcomings of 1D Otsu and 2D Otsu algorithms. Theoretical analysis shows that the method greatly enhanced the speed of thresholding. 3.2 Feature extraction and classification methods Most widely used linear feature extraction algorithms include linear discriminant analysis (LDA), principal component analysis (PCA) training algorithm and so on. The superiority of linear algorithms is its strength to reduce feature dimensionalities. Meanwhile, the limitation of these algorithms is that the decision boundaries results are linear and have less flexibility in computation. Feature extraction can be performed independently or jointly with either classification or parameter extraction. Feature extraction is the major step of lung cancer detection. System complexity increases as the number of features increases and also it reduces system performance. Several researchers have employed a wide range of features (textural features and/or morphological). These features represent some characteristics of the region that are not entirely shared by the healthy and diseased image [22, 24]. The features extracted from shape and contour properties are defined as morphological features [25]. We use an IHBF2DOT algorithm for locating and segmenting the tumour region of the lung CT images using some morphological operation from each slice. From the literature survey, it is evident that there exists lots of work in automatic lung cancer contour detection and feature extraction in CT scans. However, there exists limited work done on their comparison of feature performance. Proposed work aims to extract various discriminative features like raw, intensity and shape features and GLCM (grey-level co-occurrence matrix) to study their individual and combined performances. (i) Raw features: Features extracted from the original pixel intensity information in the ROI are called raw features. Mean intensity, max intensity, and min intensity are also calculated from the ROI. (ii) Grey level co-occurrence matrix (GLCM) features: GLCM is being utilised to estimate statistical features, and these features are used to generate the input vectors for the classifier. In the first step, GLCM is calculated. As a second step, texture features are computed based on the GLCM. Generally, GLCM exhibits pixel grey level at a fixed geometric position with respect to another pixel [26–28]. Contrast (35) Variance (36) Correlation (37)where Energy (38) Maximum probability (39) Dissimilarity (40) Autocorrelation (41) Inverse different moment (IDM) (42) Entropy (43) Homogeneity (44) Cluster prominence (45) Cluster shade (46)where (iii) FOS-based feature: Take for granted the image is a function of two different space variables x and y. this is denoted as f(x, y) [29]. Where (47)The histogram of the given image can be effortlessly computed. We can get many ideas to the character of the image from the shape of the histogram. Mean (48) Variance (49) Skewness (50) Kurtosis (51) (iv) Structural features (i) area of the tumour region, (ii) centroid value calculation, (iii) perimeter, (iv) diameter. Asymmetry index (AI): AI is computed with the following equation: (52)where A1 = non-overlapped area along a minor axis of the lung lesion, A2 = non-overlapped area along a major axis of the lung lesion and Ar = area of lesion implementation. The lesion area can be calculated by using bwarea over the segmented section of the image. The segmented region is divided along the lines passing through a centroid of the region. The perimeter of the image is also needed for symmetric value calculation. Finally, the AI is determined based on the degree of symmetric values. 3.3 Linear discriminant analysis LDA is a supervised feature extraction method to find a linear combination of features that separates two or more classes of data. The objective of LDA is to reduce the dimensionality of the data while maintaining class discriminatory information. In short, LDA aims to minimise the intra-class variations and maximise the inter-class variations. Assume a set of n-dimensional samples . Let and denote two classes with samples, respectively. Thus, if then . LDA aims to project the samples m from d dimensions onto a line and a corresponding set n of samples . Select the hyperplane that maximises the separation between multiple classes. One way to define a measure of separation (discrimination) between two classes is to find a good projection vector. Sample mean of points (53)Sample mean of projected points (54)Distance between projected means is (55)From the between-class variance and within-class variance, we can define objective function as (56)However, is not considered as a good measure since it does not consider the variance within the classes. The solution proposed by Fisher is to maximise , normalised by a factor which is proportional to the variance. Rather than forming sample variances, defi