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Soft computing-based edge-enhanced dominant peak and discrete Tchebichef extraction for image segmentation and classification using DCML-IC

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Abstract

Texture analysis is a very predominant scope in the area of computer vision and associated fields. In this work, edge-enhanced dominant valley and discrete Tchebichef (EDV-DT) method is presented to eradicate noise and segment image into number of partitions with higher accuracy and lesser time. In EDV-DT method, an edge-enhancing anisotropic diffusion filtering technique is used to perform the preprocessing for MRI, CT and texture features. The adaptive anisotropic diffusion creates scale space and reduces the image noise without removing the texture image content (i.e., edges, lines) that is found to be essential for texture image segmentation. Followed by preprocessing, histogram dominant peak valley segmentation technique is applied to segment the localization of region of interest. Valleys in histogram for the preprocessed images help in segmenting the texture image into equal-sized texture regions. Finally, with the segmented images, discrete Tchebichef moment feature extraction is applied to extract relevant features from the segmented texture image for reducing the dimensionality. This in turn helps in improving the feature extraction rate. Further a deep convolution multinomial logarithmic-based image classification (DCML-IC) model is presented for predicting results via positive and negative fact classification. The proposed system provides the better prediction of accuracy and the prediction of time to compare the other existing methods.

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References

  • Agrawal R, Sharma M, Singh BK (2018) Segmentation of brain lesions in MRI and CT scan images: a hybrid approach using k-means clustering and image morphology. J Inst Eng (India): Ser B 99(2):173–180

    Google Scholar 

  • Ahmed SA, Dogra DP, Kar S, Kim BG, Hill P, Bhaskar H (2016) Localization of region of interest in surveillance scene. Multimed Tools Appl 76(11):13561–13680

    Google Scholar 

  • Akbulut Y, Guo Y, Sengur A, Aslan M (2018) An effective color texture image segmentation algorithm based on hermite transform. Appl Soft Comput 67:494–504

    Article  Google Scholar 

  • Akcay S, Kundegorski ME, Willcocks CG, Breckon TP (2018) Using deep convolutional neural network architectures for object classification and detection within x-ray baggage security imagery. IEEE Trans Inf Forensics Secur 13(9):2203–2215

    Article  Google Scholar 

  • Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging. https://doi.org/10.1155/2017/9749108

    Article  Google Scholar 

  • Benninghoff H, Garcke H (2016) Image segmentation using parametric contours with free endpoints. IEEE Trans Image Process 25(4):1639–1648

    Article  MathSciNet  Google Scholar 

  • Borowska M, Borys K, Szarmach J, Oczeretko E (2017) Fractal dimension in textures analysis of xenotransplants. Signal, Image Video Process 11(8):1461–1467

    Article  Google Scholar 

  • Carlos C, De Zanet S, Kamnitsas K, Maeder P, Glocker B, Munier FL, Rueckert D, Thiran JP, Cuadra MB, Sznitman R (2017) Multi-channel MRI segmentation of eye structures and tumors using patient-specific features. PLoS ONE. https://doi.org/10.1371/journal.pone.0173900

    Article  Google Scholar 

  • Chaudhari P, Agrawal H, Kotecha K (2020) Data augmentation using MG-GAN for improved cancer classification on gene expression data. Soft Comput 24:11381–11391

    Article  Google Scholar 

  • Cong W, Song J, Luan K, Liang H, Wang L, Ma X, Li J (2016) A modified brain MR image segmentation and bias field estimation model based on local and global information. Comput Math Methods Med. https://doi.org/10.1155/2016/9871529

    Article  MATH  Google Scholar 

  • Cunningham RJ, Harding PJ, Loram ID (2017) Real-time ultrasound segmentation, analysis and visualisation of deep cervical muscle structure. IEEE Trans Med Imaging 36(2):653–665

    Article  Google Scholar 

  • Dong X, Shen J, Shao L, Gool LV (2016) SubMarkov random walk for image segmentation. IEEE Trans Image Process 25(2):516–527

    Article  MathSciNet  Google Scholar 

  • Gulban OF, Schneider M, Marquardt I, Haast RAM, De Martino F (2018) A scalable method to improve gray matter segmentation at ultra high field MRI. PLoS ONE. https://doi.org/10.1371/journal.pone.0198335

    Article  Google Scholar 

  • Kahali S, Sing JK, Saha PK (2019) A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation. Soft Comput 23(20):10407–10414

    Article  Google Scholar 

  • Kaplan K, Kaya Y, Kuncan M, Minaz MR, Ertunç HM (2020) An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Appl Soft Comput 87:106019

    Article  Google Scholar 

  • Karim R, Blake LE, Inoue J, Tao Q, Jia S, Housden RJ, Bhagirath P, Duval JL, Varela M, Behar JM, Cadour L, van der Geest RJ, Cochet H, Drangova M, Sermesant M, Razavi R, Aslanidi O, Rajani R, Rhode K (2018) Algorithms for left atrial wall segmentation and thickness—evaluation on an open-source CT and MRI image database. Med Image Anal 50:36–53

    Article  Google Scholar 

  • Liao W, Rohr K, Kang CK, Cho ZH, Wörz S (2016) Automatic 3D segmentation and quantification of lenticulostriate arteries from high-resolution 7 tesla MRA images. IEEE Trans Image Process 25(1):400–413

    Article  MathSciNet  Google Scholar 

  • Mercan E, Aksoyy S, Shapiro LG, Weaverx DL, Brunye T, Elmore JG (2014)Localization of diagnostically relevant regions of interest in whole slide images. In: 22nd International conference on pattern recognition

  • Mitra A, Banerjee PS, Roy S, Roy S, Setua SK (2018) The region of interest localization for glaucoma analysis from retinal fundus image using deep learning”. Comput Methods Programs Biomed 165:25–35

    Article  Google Scholar 

  • Mitra A, Tripathi PC, Bag S (2020) Identification of astrocytoma grade using intensity, texture, and shape based features. In: Das K, Bansal J, Deep K, Nagar A, Pathipooranam P, Naidu R (eds) Soft computing for problem solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore, pp 455–465

    Chapter  Google Scholar 

  • Nagabushanam P, George ST, Radha S (2019) EEG signal classification using LSTM and improved neural network algorithms. Soft Comput 24:9981–10003

    Article  Google Scholar 

  • Purkait PS, Roy H, Bhattacharjee D (2020) Local shearlet energy gammodian pattern (LSEGP): a scale space binary shape descriptor for texture classification. In: Bhattacharyya S, Mitra S, Dutta P (eds) Intelligence enabled research. Advances in Intelligent Systems and Computing, vol 1109. Springer, Singapore, pp 123–131

    Chapter  Google Scholar 

  • Rajini NH, Bhavani R (2013) Computer aided detection of ischemic stroke using segmentation and texture features. Measurement 46(6):1865–1874

    Article  Google Scholar 

  • Ribbens A, Hermans J, Maes F, Vandermeulen D, Suetens P (2014) Unsupervised segmentation, clustering and groupwise registration of heterogeneous populations of brain MR images. IEEE Trans Med Imaging 33(2):201–224

    Article  Google Scholar 

  • Rodríguez-Méndez IA, Ureña R, Herrera-Viedma E (2019) Fuzzy clustering approach for brain tumor tissue segmentation in magnetic resonance images. Soft Comput 23(20):10105–10117

    Article  Google Scholar 

  • Romero A, Gatta C, Camps-Valls G (2015) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362

    Article  Google Scholar 

  • Roy SK, Ghosh DK, Dubey SR, Bhattacharyya S, Chaudhuri BB (2020) Unconstrained texture classification using efficient jet texton learning. Appl Soft Comput 86:105910

    Article  Google Scholar 

  • Saha S, Das R, Pakray P (2018) Aggregation of multi-objective fuzzy symmetry-based clustering techniques for improving gene and cancer classification. Soft Comput 22(18):5935–5954

    Article  Google Scholar 

  • Salah MB, Mitiche A, Ayed IB (2010) Multiregion image segmentation by parametric kernel graph cuts. IEEE Trans Image Process 20(2):545–557

    Article  MathSciNet  Google Scholar 

  • Sathesh A (2019) Performance analysis of granular computing model in soft computing paradigm for monitoring of fetal echocardiography. J Soft Comput Paradig (JSCP) 1(01):14–23

    Google Scholar 

  • Shah H, Badshah N, Ullah F, Ullah A, Matiullah (2019) A new selective segmentation model for texture images and applications to medical images. Biomedi Signal Process Control 48:234–247

    Article  Google Scholar 

  • Sree SJ, Vasanthanayaki C (2020) Texture-Based Fuzzy Connectedness Algorithm for Fetal Ultrasound Image Segmentation for Biometric Measurements. In: Das K, Bansal J, Deep K, Nagar A, Pathipooranam P, Naidu R (eds) Soft computing for problem solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore, pp 91–103

    Chapter  Google Scholar 

  • Wang L, Zhang J, Liu P, Choo KKR, Huang F (2017) Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Comput 21(1):213–221

    Article  Google Scholar 

  • Yang Z, Shufan Y, Li G, Weifeng D (2016) Segmentation of MRI brain images with an improved harmony searching algorithm. Corp BioMed Res International. https://doi.org/10.1155/2016/4516376

    Article  Google Scholar 

  • Yazdani S, Yusof R, Karimian A, Pashna M, Hematian A (2015) Image segmentation methods and applications in MRI brain images. IETE Tech Rev 32(6):413–427

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of ECE, P.S.R. Engineering College, Sivakasi, India

    K. Ramalakshmi

  2. S.Veerasamy Chettiar College of Engineering and Technology, Puliangudi, India

    V. SrinivasaRaghavan

Authors
  1. K. Ramalakshmi
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  2. V. SrinivasaRaghavan
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Correspondence to K. Ramalakshmi.

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Communicated by V. Loia.

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Ramalakshmi, K., SrinivasaRaghavan, V. Soft computing-based edge-enhanced dominant peak and discrete Tchebichef extraction for image segmentation and classification using DCML-IC. Soft Comput 25, 2635–2646 (2021). https://doi.org/10.1007/s00500-020-05306-8

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