© Springer Nature Switzerland AG 2019. In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classification of malign and benign cancer tumours with a significantly improved detection precision compared to the classical HOG method as a reference when it is implemented on real time embedded system NVIDIA TX2 platform. Our approach can help to avoid unnecessary biopsies for patients and reduce the over diagnosis of clinically insignificant cancers in the future.Published versio

Aggoun, Amar

Yang, S

Yu, Z

Zhou, K

L Sun

TF Watson

M Amadasun

M Ye

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A Low Computational Approach for Assistive Esophageal Adenocarcinoma and Colorectal Cancer Detection

�� Springer Nature Switzerland AG 2019. In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classification of malign and benign cancer tumours with a significantly improved detection precision compared to the classical HOG method as a reference when it is implemented on real time embedded system NVIDIA TX2 platform. Our approach can help to avoid unnecessary biopsies for patients and reduce the over diagnosis of clinically insignificant cancers in the future.Published versio

Wolverhampton Intellectual Repository and E-theses

A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection

In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classification of malign and benign cancer tumours with a significantly improved detection precision compared to the classical HOG method as a reference when it is implemented on real time embedded system NVIDIA TX2 platform. Our approach can help to avoid unnecessary biopsies for patients and reduce the over diagnosis of clinically insignificant cancers in the future

Yu, Zheqi

Yang, Shufan

Zhou, Keliang

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     Yu, Z., Yang, S. , Zhou, K.  and Aggoun, A. (2018) A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection. Advances in Intelligent Systems and Computing, 840, pp. 169-178. (doi:10.1007/978-3-319-97982-3_14)  This is the author’s final accepted version.  There may be differences between this version and the published version. You are advised to consult the publisher’s version if you wish to cite from it.  http://eprints.gla.ac.uk/164278/                           Deposited on: 20 June 2018         Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk   A Low Computational Approach for AssistiveEsophageal Adenocarcinoma and ColorectalCancer DetectionZheqi Yu1, Shufan Yang2, Keliang Zhou2, and Amar Aggoun11 Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton,UK,2 School of Engineering, University of Glasgow, Glasgow, UK,Abstract. In this paper, we aim to develop a low-computational systemfor real-time image processing and analysis in endoscopy images for theearly detection of the human esophageal adenocarcinoma and colorectalcancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classificationof malign and benign cancer tumours with a significantly improved de-tection precision compared to the classical HOG method as a referencewhen it is implemented on real time embedded system NVIDIA TX2platform. Our approach can help to avoid unnecessary biopsies for pa-tients and reduce the over diagnosis of clinically insignificant cancers inthe future.Keywords: Machine learning, Endoscopy, Cancer detection, Textureanalysis division.1 IntroductionAlthough esophageal adenocarcinoma is uncommon, its diagnosis has increaseddramatically over the past 25 years [1]. During diagnosis process, information ob-tained from the patients physical examination, laboratory data, and endoscopicevaluation help doctors to make the correct diagnosis. While those diagnosisprocess, medical image processing analysis has become more and more popu-lar in an early stage due to low cost [2]. However, using endoscopic images todetect the lesion of esophageal adenocarcinoma and colorectal cancer is a chal-lenging task due to several facts presented in the endoscopy pictures: air bubbles,ink marking, uneven illumination and shadows [3]. Conventional medical imageprocessing algorithm design includes the following steps: image segmentation,feature extraction, reduction and classification.Artificial neural network based image segmentation approaches have drawn alot of attention due to their signal-to-noise independency. However, while thoseapproaches can achieve a high level of accuracy, they are computationally ex-pensive with less than 10 frames per second in the latest literature [4] [5]. Inthis paper, we focus on the implementation of texture analysis as feature ex-tractions into a machine learning classifier whilst balancing the computational2 Zheqi Yu, Shufan Yang, Keliang Zhou, and Amar Aggounrequirement and detection accuracy. We verify our low computational methodfor esophageal adenocarcinoma cancer detection using NVIDIA TX2 board. Ex-perimental results show that the proposed algorithm can effectively improve thespeed and accuracy by 20% compared with the HOG reference design.2 Our Texture analysis based algorithmIn this paper, we use the texture analysis to extract and analyse the image greylevels for the spatial distribution pattern. A statistical histogram of features iscalculated based on the Histograms of Oriented Gradients (HOG) [6] algorithm,followed by an AdaBoost classifier. Fig.1 shows the diagram of system structureblocks.Fig. 1. Algorithm work flow diagram.Fig. 2. Pseudo code for proposed algorithm procedure.A Low Computational Approach 3Our algorithm first uses the 3x3 kernel to calculate each pixel in the kernelto get the texture feature, and then it calculates the corresponding grey divisioninterval levels along with the kernel texture features. This process means thecentre points will be greyscaled range division when the texture feature is calcu-lating the surrounding pixels. For the statistical analysis, each pixel correspondsto a division grey interval in the statistical histogram. While the histogram iscalculating, the algorithm adds all the texture features of the same grey levelinterval and then inputs result into the AdaBoost classifier through the HOGfast integral image method. Finally, all of the weak classifiers are cascaded toobtain a strong classifier.Fig.2 is a pseudo code diagram for our proposed algorithm procedure; it hasshown the grey levels division methods for how to work in the texture analysis.3 Texture Analysis for Feature CalculationUnlike natural images, medical images include rich texture information, such aswidely used X-rays and cellular imaging. In image analysis, textures are inter-preted as a repetitive arrangement of a basic pattern (hue primitives). Thus thedescription of a texture includes the colour tones that make up the texture andthe relationship between the hues [7]. A texture is a regional feature and thusrelates to the size and shape of an area, with the boundary between two texturepatterns determined by examining the difference in greyscale pixel values [8]. Fi-nally, a texture feature is a reflection of the object structure, and therefore canprovide important image information about the object such as density, whichis an essential method of image segmentation, feature extraction, classificationand recognition.In this paper, we propose a low computational approach of only using textureinformation in greyscale images based on an Neighbourhood Grey-tone Differ-ence Matrix (NGTDM) algorithm.The NGTDM method is used to depict the pixel values relationship with itssurrounding [9]. The calculation equation is shown in equation (1).A (k, l) =1W 2 − 1[d∑m=−dd∑n=−df (k + m, l + n)]where W = 2d + 1, (m,n) 6= (0, 0)(1)The W is general settings 3 or 5 that means take the W x W window size ofthe kernel; where d specifies the neighbourhood size of pixel distance. A 3x3 (W=3) kernel is used to calculate a pixel A in the graph whose column matrix ofarray coordinates is (k, l), and the pixel grey value also corresponds to the groupsof different levels. For a two-dimension image, there should be 8 pixels aroundthe pixel A, to sum of the absolute value of the difference between a grey value(0-255) of the center pixel and the grey value of the surrounding pixels, in orderto obtain the sequence S(i) and put the result into the histogram calculation.4 Zheqi Yu, Shufan Yang, Keliang Zhou, and Amar AggounWhere f(k, l) is the grey value of the image in the window of central pixeli(k, l). S(k, l) represents the sum of the absolute values of the differences betweenthe centre pixel i and the surrounding pixels.S (k, l) =∑∣∣i−A (k, l)∣∣ for f (k, l) = i (2)In this paper, we calculate the histogram through the HOG method after thetexture feature is extracted. But the gradient vector feature of HOG is replacedby the texture features to reduce computation complexity. Three different meth-ods are proposed here: texture analysis based on HOG division (TAH), textureanalysis based on uniform grey levels division (TAD) and texture analysis basedon cubic-bezier curve grey levels division (TAC). As shown in Table 1, the TAHis a texture feature calculated directly by HOG histograms. The TAD is animproved method of replacing the gradient direction of HOG histograms. TheTAC is calculated using cubic-bezier curve division to divide the grey bin levelsof HOG histogram calculation.Histogram statistics can be used to count any image features (such as greyscale,gradient and direction). The pixel’s greyscale range contains 256 values [0 255],which are divided into sub-regions (called bins). Where n bins are divided, its’statistic pixel calculation result into a matched bin.Our proposed TAD method classifies greyscale values into ranges withoutcalculating the gradient directions. The greyscale range of 0-255 is divided intonine categories with the boundary ranges rounded down to the nearest wholenumber. Each divided range details as following:range = bin1 ∪ bin2 ∪ .... ∪ binn(nbins = 9)(3)[0, 255] = [0, 27] ∪ [28, 56] ∪ [57, 85] ∪ [86, 113] ∪ [114, 141]∪[142, 170] ∪ [171, 198] ∪ [199, 226] ∪ [227, 255] (4)The TAC is similar to the TAD method, but it has the non-uniform divisiongreyscale range to 9 bins (nbins = 9) by the cubic-bezier curve equation B(t)(equation (5)).B (t) = P0 (1− t)3 + 3P1t (1− t)2 + 3P2t2 (1− t) + P3t3 t ∈ [0, 1] (5)P0 is the starting point of the curve (0, 0)P3 is the end of the curve (255,255)P1 and P2 are the control points for the trend of the curve, so any changing bythese two parameters.t is a dummy parameter that acts on all connected lines of points.A Low Computational Approach 5Table 1. Division Calculation Complexity Comparison table.Types FeatureDivision CalculationComplexityHOGHistogram of OrientedGradient of 3x3 cells(cell similar to kernel)gradient magnitudeand gradient directionThe gradient of each pixel,including size and direction,all of the cell of gradients aredivided into 9 bins that to get9 dimensional feature vector.Twice addition and subtraction+Once division calculation+Once arc-tangent function(mainly time consumption)+Once multiplication calculationTAHTexture analysisfor a 3x3 kernelTADCentral pixel of valueto continuous uniformdivision for 9 binsOnce multiplication calculation+OnceshiftoperationTACCentral pixel of valueto cubic-bezier curvedivision for 9 binsThe cubic-bezier also is called as a third-order Bezier; it is a classic methodof curve approximation. In the endoscopic image analysis, the appearance ofcancer, illumination condition changes and the background of the image haverelatively small variability. The high and low light regions can be set up asdifferent greyscale divided interval by curve regulation. Such as the highlightedarea for low recognition rate, where the curve can be set smooth or steep toenhance the effect. And also, the same applies to low light area settings. Thereis an advantage than the uniform division that more suitable for the endoscopicenvironment.The comparison of various features is shown in the following Table 1. Fourmethods are based on different classification and feature extraction. The HOGand TAH methods are both for using HOG division method for feature extrac-tion. It uses greyscale pixel values for vector computation to separate the vectorspace results (vector directional angles) and to calculate gradient magnitude andgradient direction to histogram statistic. We can find on the TAD and TAC di-vision methods that the kernel calculation is a texture analysis to the histogramstatistics. It can achieve the greyscale values by calculating the difference valuesof pixels.4 Experiments Results and DiscussionsThe experiment uses OpenCV 2.4.13 library version of AdaBoost traincascadeprogram interface and runs on CPU i7 4710MQ for training and testing of the6 Zheqi Yu, Shufan Yang, Keliang Zhou, and Amar Aggounabove four methods. The final experiments are implemented on NVIDIA TX2platform. We choose and set the 5 scale rates for the AdaBoost that are 1.05,1.1, 1.3, 1.5 and 1.9. In order to eliminate differences from the OpenCV programoptimisation for above four methods, we run the experiments using a unifiedsingle-threaded processing. We use VIVO Endoscopic Video Datasets [10] totrain and test our proposed algorithm.4.1 Quality resultsFig. 3. Computer simulation results.After getting the statistical texture information, we need to decide the sizeof the bounding box (Region of Interests). A K −means clustering algorithm isthen used for clustering analysis esophageal adenocarcinoma cancer size on theendoscopy image that to sets a suitable detection window size, it is concludedto be 80 (width) x 60 (height), which is to obtain statistically the number ofboxes (number of clusters) and the object size (cluster centre box). A simpleAdaBoost classifier was online trained. However first experiments results showthat the false detecting rate is too high due to the inefficient AdaBoost classifier,as shown in the Fig.3D.In the second experiment, a cascade AdaBoost classifier uses the sliding win-dow to find the objects in different positions of the image and magnify theA Low Computational Approach 7detection window to find the different size objects in the image with a thresh-old banding. The final object box is the highest score of the levelWeight bycomparison. The final detection effect is shown on the Fig.3E.4.2 Quantitative AnalysisTable 2 demonstrates the detection performance including precision and recall.For the precision results, there is many similarities, but the TAC algorithm morestable for each scale rate and it is the top average in the precision. For the recallresults, the HOG division method (HOG and TAH) has a significant differenceand lower than our design algorithm. The TAC is still top one in the averagerecall and each scale rate better than 10% with HOG division method. Theresult of comprehensive speed and recognition rate can meet the requirementsof real-time operation for the algorithm after the 1.3 scale rate of AdaBoost.Moreover, in the embedded system test, the TAC method can be achieved inreal time processing in the 1.9 scale rate (shown in the TACT results). The TACmethod clearly dominates the best effect at this time scale rate.Table 2. Precision and Recall Comparison tableType ResultsAlgorithmScale rate HOG TAH TAD TAC TACTPrecision (%)1.05 65.4 49.7 60.5 64.3 61.51.1 77.1 58.4 62.7 67.2 63.41.3 75.1 67.6 68.7 71.8 64.11.5 39.1 60.6 68.2 70.9 64.51.9 66.4 61.2 66.8 66.6 59.5Avg-Precision 64.42 59.50 65.38 68.16 62.60Recall (%)1.05 50.1 37.1 59.5 61.3 59.51.1 55.9 48.7 62.8 63.5 62.01.3 59.4 52.4 66.3 65.8 60.11.5 39.7 45.4 60.6 63.7 56.61.9 44.2 37.1 53.5 52.5 47.8Avg -Recall 49.86 44.14 60.54 61.36 57.20Fig.4 has compared the average processing speed. All of test videos andpictures are based on 640x480 resolution. In the 1.9 scale rate, the grey valuedivision method is 10% faster than the HOG algorithm. Also, the TACT resultshave shown the embedded systems achieved real time processing that over the25fps in the TX2 board.Fig.5 has shown four algorithms of 9bins histogram statistics for the same im-age. By comparison, the TAC method of the histogram is outperformed by usingthe cubic-bezier curves for non-uniform greyscale range division. So that meanseach bin in the TAC is including a different greyscale range for the matched non-uniform grey interval and that can improve histogram differences. The TAH and8 Zheqi Yu, Shufan Yang, Keliang Zhou, and Amar AggounTAD methods of the histogram are smooth and stable which means there is litterdifferent in features.Fig. 4. Algorithm fps results.4.3 DiscussionThe TAC algorithm achieves the highest recognition rates, particularly whenusing higher scale rates compared to HOG algorithms. The HOG algorithmsproduce high levels of accuracy in esophageal cancer detection but have lowrecall rates (less than 50%). The TAH directly using texture features to matchthe official HOG calculation method, not only leads to a decrease in accuracy,but the processing speed also drops dramatically and as a result is unable tomeet the requirements of the real-time processing. The use of the greyscaledivision method between the texture analysis and histogram, not only showsa higher accuracy (large-scale rate of AdaBoost setting) than the HOG method,but also a greater speed. In two Greyscale division methods, it has also beenshown the best results from a non-uniform division using cubic-bezier curves. Itmeans that in the textural analysis of cancer detection, the non-uniform rangeof greyscale values is good for histogram statistics. Therefore, it is possible thatusing the non-uniform intervals of greyscale values calculated by the cubic-beziercurve is better than the HOG algorithm for esophageal cancer detection. InA Low Computational Approach 9Fig. 5. Algorithm histogram results.addition, in the embedded system, the processing without vector feature canreduce computation that helps it running fast and easy to achieve real timeprocessing.However, when using small-scale rates, the HOG method still provides thebest accuracy using multi-feature calculation (vector of gradient and angle). Thismeans that the texture analysis method for feature extraction mainly benefitsfrom using a simplified calculation to reduce processing time, whilst sacrificingaccuracy as a result. Our proposed TAC method has improved accuracy overtraditional texture analysis methods, but is still limited by its use of simplifiedcalculations in texture analysis.5 ConclusionIn this paper, we proposed a supervised machine learning method integratedwith texture analysis for medical image processing. We compared three differ-ent HOG based feature extraction techniques with our proposed non-uniformdivision method, and verified them in the embedded system platform. The com-putational cost of the HOG based technologies in supervised learning is high dueto the processing needs for vector-based feature extraction. Our method takesadvantage of dividable uneven greyscale levels presented in texture features toreduce the computational complexity of machine learning, enabling higher pro-cessing speed per second.10 Zheqi Yu, Shufan Yang, Keliang Zhou, and Amar AggounReferences1. Lucy Sun, Amy F. Subar, Claire Bosire, Sanford M. Dawsey, Lisa L. Kahle, Thea P.Zimmerman, Christian C. Abnet, Ruth Heller, Barry I. Graubard, and Michael B.Cook. Dietary flavonoid intake reduces the risk of head and neck but not esophagealor gastric cancer in us men and women. The Journal of nutrition, 147(9):1729–1738, 2017.2. Priya Darshini Velusamy and Porkumaran Karandharaj. Medical image processingschemes for cancer detection: A survey. In Green Computing Communication andElectrical Engineering (ICGCCEE), 2014 International Conference on, pages 1–6.IEEE, 2014.3. TF Watson, MAA Neil, R. Jukaitis, RJ Cook, and T. Wilson. Videorate confocalendoscopy. Journal of microscopy, 207(1):37–42, 2002.4. Jin Qi, Miao Le, Chunming Li, and Ping Zhou. Global and local information baseddeep network for skin lesion segmentation. arXiv preprint arXiv:1703.05467, 2017.5. Zhen Yu, Xudong Jiang, Tianfu Wang, and Baiying Lei. Aggregating deep convo-lutional features for melanoma recognition in dermoscopy images. In InternationalWorkshop on Machine Learning in Medical Imaging, pages 238–246. Springer, 2017.6. Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detec-tion. In 2005 IEEE Computer Society Conference on Computer Vision and PatternRecognition (CVPR’05), volume 1, pages 886–893. IEEE, 2005.7. Nalini Bhushan, A. Ravishankar Rao, and Gerald L. Lohse. The texture lexicon:Understanding the categorization of visual texture terms and their relationship totexture images. Cognitive Science, 21(2):219–246, 1997.8. Peter W. Hawkes. 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A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection

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