In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.
Description:
I.J. Image, Graphics and Signal Processing, 2013, 8, 29-36
Poulose Jacob,K; Binsu, Kovoor C; Supriya, M H(International Journal of Computer Science and Communication, June , 2011)
[+]
[-]
Abstract:
Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to
identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant
interest in the wake of heightened concerns about security and rapid advancements in networking, communication
and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and
independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures
has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound
tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures
data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector
Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using
Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared
to individual unimodal recognition systems
Description:
International Journal of Computer Science and Communication Vol. 2, No. 1, January-June 2011, pp. 143-147
Kannan, Balakrishnan; Jomy, John; Pramod, K V(MECS, April , 2013)
[+]
[-]
Abstract:
In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets
Description:
I.J. Image, Graphics and Signal Processing, 2013, 4, 53-59
Mythili, P; Baby, Paul; Shanavaz, K T(IEEE, January 3, 2012)
[+]
[-]
Abstract:
In this paper an attempt has been made to determine
the number of Premature Ventricular Contraction (PVC) cycles
accurately from a given Electrocardiogram (ECG) using a
wavelet constructed from multiple Gaussian functions. It is
difficult to assess the ECGs of patients who are continuously
monitored over a long period of time. Hence the proposed
method of classification will be helpful to doctors to determine
the severity of PVC in a patient. Principal Component Analysis
(PCA) and a simple classifier have been used in addition to the
specially developed wavelet transform. The proposed wavelet has
been designed using multiple Gaussian functions which when
summed up looks similar to that of a normal ECG. The number
of Gaussians used depends on the number of peaks present in a
normal ECG. The developed wavelet satisfied all the properties
of a traditional continuous wavelet. The new wavelet was
optimized using genetic algorithm (GA). ECG records from
Massachusetts Institute of Technology-Beth Israel Hospital
(MIT-BIH) database have been used for validation. Out of the
8694 ECG cycles used for evaluation, the classification algorithm
responded with an accuracy of 97.77%. In order to compare the
performance of the new wavelet, classification was also
performed using the standard wavelets like morlet, meyer,
bior3.9, db5, db3, sym3 and haar. The new wavelet outperforms
the rest
Description:
Power, Signals, Controls and Computation (EPSCICON), 2012 International Conference on,pp 1-5