Early Stage Oral Cavity Cancer Detection: Anisotropic Pre-Processing and Fuzzy C-Means Segmentation

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1 Early Stage Oral Cavity Cancer Detection: Anisotropic Pre-Processing and Fuzzy C-Means Segmentation Zhalong Hu 1 *, Abeer Alsadoon 1 *, Paul Manoranjan 2*, P.W.C. Prasad 1*, Salih Ali 3 * 1 School of Computing and Mathematics, Charles Sturt University, Sydney, Australia. 2 School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia. 3 University of Technology Baghdad, Baghdad, Iraq. Abstract The high rates of oral cavity cancer incidence have been found worldwide over the past decade. The death rate from oral cavity cancer is high and increasing. This study aims to improve the tumour diagnosis accuracy in the oral cavity, duly considering image processing time. It has focused on oral Computed Tomography (CT) image pre-processing and segmentation steps to enhance image quality and clarity to improve classification result. The proposed system focused on image pre-processing and segmentation steps, using anisotropic diffusion and Fuzzy C-Means to enhance the quality of the image, then improve the accuracy of tumour detection and classification. The findings attained from the current solution are based on a proposed approach using Support Vector Machine (SVM) as the traditional machine learning method to classify the oral tumour. With the combination of the anisotropic filter and fuzzy c-means algorithm, the proposed approach achieved 90.11% accuracy, 87.5% specificity and % sensitivity rate whereas the accuracy rate of the selected current best solution is only 87.18%, This study contributes to current research mainly through the implementation of an algorithm that is able to identify small sized early tumours in image edge areas. Keywords Tumour classification, edge detection, oral and maxillofacial surgery, image preprocessing, image segmentation, Anisotropic Diffusion, Fuzzy C-Means. I. INTRODUCTION Oral cancer is one of the most common types of cancer. Like other types of cancer, it is an essential threat to human's life. The death rate from oral cavity cancer is high and increasing [1]. It is essential to discover and implement a method to diagnose these tumours in the early stages of oral cancer. Appropriate algorithms for diagnosing and identifying potential tumours on CT images in the early stages can significantly increase the patient s cure and survival rate [2]. Current diagnostic methods are generally based on physical exams and biopsies. Automatic imaging classification has been used to assist doctors to look for suspected tumour [3]. This study is focused on accuracy and processing time for segmentation and classification of benign or malignant tumours based on the latest research, while, improving the classification accuracy in the edge area of the tumour image. The algorithms proposed in this study assist with accurate diagnosis of types of tumours. The proposed study focuses on two main sections: (i) to identify recent research and to find the latest methodology to reduce the complexity of the current best solution and improve the accuracy of tumour diagnosis through image processing efficiency; (ii) to implement the proposed solution, compare and validate the proposed design to improve the present best solution. II. LITERATURE REVIEW A. Techniques for Diagnosing Oral Cancer, The automatic segmentation algorithm based on the symmetric axis analysis has proposed, which is an analysis of the left and right sides of each image [3]. The aim was to locate the difference in intensity where the tumour exists. This study achieved high true positive results including various types of cysts. Nevertheless, the detection failure will occur if the tumour is in the middle or on either side of the image. The pre-processing method, an anisotropic diffusion filtering, used in this study reduced the noise on the CT image meanwhile enhancing the edge region in the oral cavity. In the further study, [3] integrated and improved their previous study into the classification of oral cysts. The result achieved 96.48% accuracy rate in 96 samples with three categories of oral cysts. However, this research is based on the previous study and the critical issue is the segmentation method. There is a concern that this study was not tested against a large image database. In such a case, the accuracy rate may be quite different. These two continuous studies have been marked as second-best solutions. A hybrid method by combining Fussy C-Means and neutrosophic algorithm for segmentation of tumours in the oral panoramic image proposed [4]. It used speckle reduction by 3 3 size median filter to reduce the speckle noise, then reducing image noise with the neutrosophy algorithm.. This approach provided a significant improvement in segmenting oral lesions. The accuracy comes from the use of indeterminacy degree to cluster the region and determination of the tumour. However, as this study is based on cluster calculation and image boundary location, shadow areas on the panoramic image will be a concern in terms of false detection. A study utilised variants of SVM, such as Linear SVM, Quadratic SVM and Cubic SVM, to classify tumours on

2 optical coherence tomography images. They compared the sensitivity, specificity and accuracy in 6 different classification conditions [5]. They found the highest solution. This research is based on biopsy images, and the accuracy is coming from the high quality of the images. Thus, it is clear that low-quality or noisy images would be a concern. A similar study by using hybrid feature extraction and machine learning classification algorithm with biomarkers. They proposed and tested five tumour classification methods [6]. The method, ANFIS, achieved the highest classification rate of 93.81% by combining the clinicopathologic dataset and biopsy images. This study considers patient s case as a whole. However, if the clinicopathologic dataset or image are considered in isolation, this solution will not achieve over 90% accuracy rate. In other words, the accuracy of this method is relying on the patient s information. An automated oral lesion detection method has been studied [1]. It discussed two systems to discover two types of common lesions in the oral cavity. It achieved 92% sensitivity with 0.32 false positives on average in close border lesions and 85% sensitivity with no false positives in open border lesions. Moreover, it also discussed the possibility of improvement in open border lesion algorithm to 100% sensitivity with only 0.13 false positives. A new SVM model named RF-SVM proposed [7]. It provides the highest (83.58%) accuracy in identifying Inferior Alveolar Nerve Injury compare to three other SVM algorithms and medical experts. This research used anisotropic diffusion filters to remove noise [3]. This algorithm is providing a quality filtered image result by removing noise and enhancing the edge and boundaries. A SVM approach for oral tissue cell classification has proposed [8]. In this research, linear SVM classifier to identify normal and abnormal oral cells in biopsy image was used. The classifier classifies the linearly separable and non-separable data first, then classifies cells on pixels size images. Every step to process the CT image and classify the tumour using hybrid algorithms has applied, whilst achieving high accuracy (87.18% classification rate) by using a combination of three algorithms, FO GLCM and GLRLM [9]. It achieved a very high rate of Area Under the Receiver Operating Characteristic (AUC) at 94.44%. The Above mentioned methods implemented one or two parts of the features in image processing steps Pre-processing, Segmentation, Features Extraction and Classification. In other words, they are focusing on improving each processing step separately. Most of the recent research achieve relatively high percentages of accuracy and low error rates. B. Current Best Solution Compares to all these studies, the best present solution is proposed by Nurtanio [9]. This study clearly introduces the image processing method used in each process stage. The combination of the algorithms (Figure 1) used in this paper yielded high accuracy and low error rates. In the current solution (Figure 1), a Gaussian filter was used to pre-process the dental panoramic image to smooth the image and reduce the noise. Further, the image was segmented with the selection of Region of Interest (ROI) algorithm (Figure 1). Them the three different algorithms were added: first-order statistics texture (FO), Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM), to extract features (Figure 1). Finally, the researchers used the SVM (Figure 1) to classify and evaluate the results via receiver operating characteristic curve and compute the AUC [9]. In this solution, the combination of FO and GLRLM algorithms provided high accuracy and achieved the highest AUC. The first order statistics texture analyses the intensity of the histogram on the image [10]. The gray level run length matrix [11] is extracting and analysing the texture features by using a matrix to understand a particular direction of the adjacent pixels with the same grey intensity pixels The current solution challenges are presented in figure 1 inside the red border. First, considering the compound and complexity of the algorithms (Figure 1) in this study, the processing time will not be short. Usually, it considers the linear number of features and constant in the training data. Second, the edge area enhancement is not considered in this solution (Figure 1). It needs to be a concern that tumours might appear in the edge area, especially small ones in the early stage. The 2D Gaussian filter (Equation 1) is used for image preprocessing in the current solution. It is widely used in image processing. The x, y is the uncorrelated variates which shows a bivariate normal distribution; is the blob spread which is a standard deviation (Equation 1). The pseudocode of Gaussian is in Table 1. However, the Gaussian algorithm (Figure 1) is smoothing the image by blurring it. This means the boundaries on the original image would blur, increasing the difficulty to find the boundaries and differences compared to the original picture. (1) III. PROPOSED MODEL To improve the current best solution, we analysed and compared different algorithms, and identified a second best solution to solve the limitations in the current solution. A new hybrid solution (Figure 2) has been proposed to improve oral cancer detection accuracy, specificity and sensitivity. In the proposed model (Figure 2), the focus is on - image preprocessing and segmentation areas. In the pre-processing, to reduce the image noise, while simultaneously enhancing image quality, this study is using anisotropic diffusion filter (Figure 2) instead of the Gaussian filter to improve the accuracy (Figure 1). The proposed algorithm can remove noise and improve image quality by keeping all the edges clearly visible. In the segmentation stage, the use of the fuzzy c-

3 means (Figure 2) is aimed at improving the accuracy and specificity which would affect results. The other methods used in features extraction and classification remain the same as the current best solution (Figure1). pseudocode in Table 2. We developed the processing method to transfer the original image file to a 2D matrix, then processing data points with diffusion; it will return an updated 2D matrix which can present the processed image. This improvement leads to the success of the following steps and the final results. Fig. 2. Proposed Model Fig. 1. Current Best Solution A. Image Pre-Processing The original CT image always included some noises for a range of reasons. These noises will affect the accuracy of the diagnosis. Pre-processing is an important step in image process. The anisotropic filter is a filter originally introduced [12] which now is widely used for enhancement of grayscale images by reducing noise and enhance the structure of images, especially edge boundaries. The general anisotropic filter equation is: The x, y, t in Equation 2 are the control rate of diffusion coefficient. It is presenting the image gradient which is used for preserving the edges in the image. denotes the gradient, div() is the divergence operator (Equation 2). denotes the Laplacian and I is the grey scale image matrix. To optimise this equation, we transferred the diffusion tensor as D, and trace is the image derivative kernel matrix which is used in our work (Equation 3). The equation can be rewritten as: In our work, we used the anisotropic diffusion, based on the first derivative kernels of 3 3 3, and the second derivative kernels would be 5 5 5, which is the tracing area. Setting these two kernels results in faster processing speed and clear denoised images. The algorithm is developed with a (2) (3) B. Image Segmentation Fuzzy c-means is an algorithm clustering all the data points. In this case the image pixels into several cluster groups (Equation 4). The i and j are the data point location in the matrix. The membership of u ij (Equation 5) will be calculated using: To compute the centre c j, we put the membership u ij into the following (Equation 6): The result of each data points in clusters can overlap, not must belong to one cluster centre. This means membership of each data point can assign to the different cluster centre (Equation 5). It would provide a better result than the other mean algorithms. k is the step of iteration. U is the matrix of fuzzy membership C. Feature Extraction First-order: is widely used for image texture analysis by calculating the intensity of histograms. With the FO algorithm six features can be extracted including mean, (5) (6) (4)

4 standard deviation, smoothness, third moment, uniformity and entropy [9]. Gray Level Run Length Matrix (GLRLM): is a texture features analysis matrix which can be used to match the gray pixels from the reference pixels as a pattern with a particular direction. The run length is the length of the particular direction of pixels with the same gray intensity. The matrix can extract seven features including short runs emphasis, long runs emphasis, gray level non-uniformity, run percentage, run length non-uniformity, low gray level run emphasis and high gray level run emphasis [9]. D. Classification An SVM classifier, the binary classification method. The classification is mapping the original data points from the original space to a higher dimensional feature space [9]. IV. EXPERIMENTAL RESULTS AND DISCUSSION This research prepared a dataset of 91 patients cases with over 600 CT images, including various types of tumour and cyst lesions in 56 positive cases. The image dataset is retrieved from The Cancer Imaging Archive (TCIA). The evaluation of the image cases was done by a radiologist working at a hospital in Hangzhou, China. DICOM image is a stand image format for Digital Imaging and Communications in Medicine which is widely used for MRI, CT scan and ultrasound images. In this study, all the images are in DICOM format and based on CT scan. All the images are pixels. Running environment is using Python version 2.7 on macos with MacBook Pro (Processor: 2.7 GHz Intel Core i5, Memory:16 GB). As shown in Table 1, the 10 sample images were randomly selected from the image dataset of 91 patients cases. The processing times for each step were established and the classification results of different images with various types of tumour were obtained. The average time for pre-processing is reduced. However, the average of the following steps is increased second. The main reason is Gaussian filter blurred the image which results in the less calculation time for segmentation and features extraction. The sample 1, 3, 8 and 9 showed differences in results between the current best and proposed solution. Compared with these images, one of the reasons is that the tumour is small and the intensity is low on the image. Meanwhile, the Gaussian algorithm smoothed the image and blurred the tumour boundaries, making it even harder to identify and classify. The final test result were calculated from 91 cases shown in Figure 3. Test results were then compared with the current and the proposed solution using the same features extraction and classification algorithms. For the following equations 5, 6, 7, the TP is the number of true positives, FP is the number of false positives, TN is the number of true negatives and FN is the number of false negatives. The accuracy equals the sum of true positives and true negatives. Specificity means the true negatives divided by true negatives and false positives. Percentages for the final overall results were calculated based on Equation 5, 6 and 7 and illustrated in Table 2. The proposed solution improved the accuracy from 86.81% to 90.11% (Table 5) with the combination of FO and GLRLM algorithms in features extraction. It means the proposed preprocessing and segmentation method which improved the image quality and cleared boundaries helps to improve the final result. Based on the result of implementation, the proposed method improved the accuracy, specificity and sensitivity by pre-processing the image with anisotropic diffusion filter and segmenting the processed image with fuzzy c-means. Through enhancing the quality of the images and clearing all boundaries, the result of the proposed solution achieved the highest 90.11% accuracy, 87.5% specificity and 92.16% sensitivity (Table 1). The accuracy equals the sum of true positives and true negatives. Specificity means the true negatives divided by true negatives and false positives. Percentages for the final overall results were calculated based on Equation 5, 6 and 7 and illustrated in Table 2. The proposed solution improved the accuracy from 86.81% to 90.11% (Table 5) with the combination of FO and GLRLM algorithms in features extraction. It means the proposed preprocessing and segmentation method which improved the image quality and cleared boundaries helps to improve the final result. Based on the result of implementation, the proposed method improved the accuracy, specificity and sensitivity by pre-processing the image with anisotropic diffusion filter and segmenting the processed image with fuzzy c-means. Through enhancing the quality of the images and clearing all boundaries, the result of the proposed solution achieved the highest 90.11% accuracy, 87.5% specificity and 92.16% sensitivity (Table 1). V. CONCLUSION CT images provide clear and structured diagnostic information to diagnose tumours. According to the present best study, the methodologies and algorithms used achieved high accuracy. However, there were still some limitations in these solutions that needed to be improved by combining different solutions. Accuracy, false classification and efficiency show possibilities for further study and improvement. The first challenge in this (5) (6) (7) (5) (6) (7)

5 research was finding an efficient and suitable algorithm to improve the oral tumour classification accuracy, especially tumours in the edge area and small tumours in the early stages of the disease. We compared and analysed different algorithms through the literature reviews, selected a number of best solutions and tested them with pseudocode, selecting two as possible solutions. Second, to label the anonymous patients case image dataset, we consulted a radiologist working in the Sample ID Original Image Type of Cancer Pre-process Table 1 Test Running for each step and final detects results Segmentation Current Solution First Order Process FO + GLRLM Process hospital who manually labelled all cases. The proposed solution uses anisotropic diffusion filter for pre-processing images by remove the noise and enhancing the boundary. Then fuzzy c-means was used to segment the image, followed with first order and grey level run length matrix, then classifying of the oral tumour. The proposed method achieved excellent accuracy (90.11%). Furthermore, results demonstrated that even small size tumours can be detected. Tumour Detected Results Accuracy Pre-process Segmentation Proposed Solution First Order Process FO + GLRLM Process Tumour Detected Accuracy 01 Fibroma P F N T P T P T 03 Papilloma N T P F P T P T Verrucous Odontogenic tumors Minor salivary gland s P T P T N T P T P T 08 Lymphomas N T P F P F P T P T Average P: Positive; N: Negative; T: True; F: False N N T T Accuracy Specificity Sensitivity % 0.9% 0.882% 0.921% Fig. 3. Final Test Result Table 2 Test Results Current Solution Proposed Solution Methods FO FO+GLRLM FO FO+GLRLM REFERENCES [1] S., Galib, F., Islam, M., Abir& H.-K., Lee,. Computer aided detection of oral lesions on CT images, Journal of Instrumentation, 10(12), [2] N. P., Malek, S., Schmidt, P., Huber, M. P., Manns, & T. F., Greten, The diagnosis and treatment of hepatocellular, Alcohol, 20, [3] F., Abdolali, R. A., Zoroofi, Y., Otake, & Y., Sato, Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics, Computer Methods and Programs in Biomedicine, 139, , [4] M. K., Alsmadi, A hybrid Fuzzy C-Means and Neutrosophic for jaw lesions segmentation, [Press release], Retrieved from [5] S., Banerjee, S., Chatterjee, A., Anura, J., Chakrabarty, M., Pal, B., Ghosh,J.,Chatterjee, Global spectral and local molecular connects for optical coherence tomography features to classify oral lesions towards

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