Unsupervised image

The data are dominated by a few low molecular weight components. Figure 15.11 presents an image that has been amplified by a factor of 30 many more components are visible. Figure 15.12 presents the same data amplified by another factor of 30 for a total amplification of 1000, and a sea of peaks is visible. An unsupervised routine was used to isolate all local maxima in the data 190 components were resolved with amplitude greater than 10 times the standard deviation of the background signal. 

Pattern recognition can be classified according to several parameters. Below we discuss only the supervised/unsupervised dichotomy because it represents two different ways of analyzing hyperspectral data cubes. Unsupervised methods (cluster analysis) classify image pixels without calibration and with spectra only, in contrast to supervised classifications. Feature extraction methods [21] such as PCA or wavelet compression are often applied before cluster analysis. 

Bambery, K., Wood, B. R., Quinn, M. A. and McNaughton, D. (2004) Fourier Transform Infrared Imaging and Unsupervised Hierarchical Clustering applied to cervical Biopsies. Aust J. Chem. 57, 1139-43. 

Other classical unsupervised cluster analysis methods rely on using mathematical indicators, such as distances, to quantify the similarity among pixel spectra. Thus, each pixel can be viewed as a point in the space of original wavenumbers or on other spaces, for example PC space. The coordinates of a pixel can be the spectral readings at the different wavenumbers (in the original image space) or the scores (in the PC space). Similar pixels should be close in the reference space and, therefore, distance measurements, such as Euclidean distance ( ), can be used to assess this proximity ... 

Although supervised and unsupervised methods have been in the past well-separated families, a number of hybrid approaches have appeared that combine both unsupervised and supervised classifiers for image segmentation purposes [61]. 

Unsupervised analyses are often performed on unknown samples in order to obtain qualitative results. The results shown in Figure 11.9 can be considered the final results here, the sample consisted of at least four chemical components-caffeine, acetaminophen, aspirin and cellulose-and their distributions were visualized in Raman images. 

Pattern Classification. These methods processed the information of the features of the image. There are two main approaches, one that use a labeled sample also called supervised and another that use unlabeled samples called unsupervised. 

This method are widely applied in medical imaging as a classifier, in which the weights are determined by using training data and the ANN is then used to segment new data. ANNs can also be used in unsupervised way as a clustering method. Because of the many interconnections used in a neural network, spatial information can be easily incorporated into its classification procedures [7]. 

The fuzzy C-mean (FCM) approach (Udupa and Samarasekera 1996 Bezdek 1948) is able to make unsupervised classification of data in a number of clusters by identifying different tissues in an image without the use of an explicit threshold. The FCM algorithm performs a classification of image data by computing a measure of membership, called fuzzy membership, at each pixel for a specified number of classes. The fuzzy membership function, con-... 

In this classification numerical operations are performed that search for natural groupings of the spectral properties of pixels as examined in an image. The computer selects the mean class and covariance matrices to be used in classification. Once the data is classified, the classified data are assigned to some natural and spectral classes and the spectral classes are converted to information classes of interest. Some of the clusters are meaningless as they represent mixed classes of earth surface materials. The unsupervised classification attempts to cluster the Dn values of the scene into natural boundaries using numerical operations. 

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