Abstract
Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagnosis based on classification of the normal and abnormal images of computed tomography. The computed tomography images used in the classification consists of non-enhanced computed tomography images. The proposed method consists of four stages namely pre-processing, feature extraction, feature reduction and classification. The discrete wavelet transform coefficients are the features extracted in this method. The essential coefficients are selected by the principal component analysis. The features derived are used to train the binary classifier, which infer automatically whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. The proposed method has been evaluated on a dataset of 80 images. A classification with a success of 92, 97 and 98 % has been obtained by artificial neural network, k-nearest neighbor and support vector machine, respectively. This result shows that the proposed technique is robust and effective.
Similar content being viewed by others
References
Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346:1135–1138
Chawla M, Sharma S, Sivaswamy J, Kishore LT (2009) A method for automatic detection and classification of stroke from brain CT images. Conf Proc IEEE Eng Med Biol Soc:3581–3584
Cover TM, Hart PE (1967) Nearest neighbour pattern classification. IEEE Trans Inf Theory 13(1):21–27
Cristianini N, Shawe Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods, 1st edn. Cambridge University Press, New York
Doi K (2005) Current status and future potential of computer-aided diagnosis in medical Imaging. Br J Rad 78(Spl issue):S3–S19
Fallahi AR, Pooyan M, Khotanlou H (2010) A new approach for classification of human brain CT images based on morphological operations. J Biomed Sci Eng 3:78–82
Gong T, Li S, Wang J, Lim C, Pang TBC, Tchoyoson Lim CC, Lee Qi Tian CK, Zhang Z (2011) Automatic labeling and classification of brain CT images. In: 18th IEEE international conference on image process (ICIP), pp 1581–1584
Jolliffe IT (1986) Principal component analysis. Springer, Berlin, p 487
Kidwell CS, Wintermark M (2010) The role of CT and MRI in the emergency evaluation of persons with suspected stroke. Curr Neurol Neurosci Rep 10(1):21–28
Liu B, Yuan Q, Liu Z, Li X, Yin X (2008) Automatic segmentation of intracranial hematoma and volume measurement. In: Proceedings of the 30th annual international conference on IEEE EMBS, pp 1214–1217
Mallat S (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Pattern Anal Mach Intell 11(7):674–693
Padma A, Sukanesh R (2011) Automatic classification and segmentation of brain tumor in CT images using optimal dominant gray level run length texture features. Int J Adv Comp Sci Appl 2(10):53–59
Peng F, Yuan K, Feng S, Chen W (2008) Region feature extraction of brain CT image for classification. In: 2nd IEEE international conference on bioinformatics and biomedical engineering, pp 2495–2498
Perez N, Valdes J, Guevara M, Silva A (2009) Spontaneous intracerebral hemorrhage image analysis methods: a survey. Adv Comput Vision Med Image Process 13:235–251
Zhang WL, Wang XZ (2007) Feature extraction and classification for human brain CT images. In: Proceedings of the IEEE international conference on machine learning and cybernetics, vol 2, pp 19–22
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hema Rajini, N., Bhavani, R. Automatic classification of computed tomography brain images using ANN, k-NN and SVM. AI & Soc 29, 97–102 (2014). https://doi.org/10.1007/s00146-013-0442-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00146-013-0442-6