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The performance of automated analysis of cell level microscopic images depends profoundly on the features extracted to represent the characteristics of the nuclei. The foremost features that signify the nuclei objects are;

textural, colour and morphological features. These features have been

explored through various studies in an attempt to investigate and determine the optimal nuclei features. Diverse methods have been employed to extract nuclei features (Liu, 2011) and use a set of features to classify cell nuclei in fluorescent microscopy images. A variety of features such as morphological features, Zernike moments, (Boland and Murphy, 2001), Haralick texture features (Doyle et al., 2007) and wavelet features (Doyle et al., 2008) were used to extract features of the cell. Plissiti et al. (Plissiti et al., 2011) used a combination of shape, texture and intensity features for cell nuclei extraction in Pap smear images. To define shape features, the authors used a number of cell properties such as circularity, perimeter, eccentricity, and the major and the minor axis length. Statistical properties of the intensity histogram were extracted to define the textural features. Finally, intensity features were extracted based on the average intensity value of the pixel. This approach had the advantage of integrating a variety of features concerning the morphological characteristics of the nuclei as well as the textural and intensity features. However, colour features were not considered in this study.

Plissiti et al. (Plissiti et al., 2011) proposed an approach for extracting the colour and textural features in the spectral domain to recognize centroblasts

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in histopathological images. The variation of the power spectrum along the frequency scale was used to extract textural features of the centroblasts. Principal component analysis (PCA) was used to extract colour texture features in the spectral domain. Finally, a quadratic discriminant analysis (QDA) classifier was used to classify the centroblasts and the non- centroblasts. Although colour features were considered in this study, PCA was used to extract the colour features, which was shown to produce ineffectual results when compared to Discrete Wavelet Transform (DWT) for colour features extraction. The use of DWT of different types in image feature extraction is reviewed below;

Discrete Wavelet Transform (DWT) is a multi-resolution technique used for various image processing purposes such as image compression, texture and colour analysis (Daubechies, 1990; Kokare et al., 2007; Sun and Ozawa, 2003). It has also been utilized in the medical imaging field. Gabor Wavelet was used to extract features from mammograms (Buciu and Gacsadi, 2009). The features were used to discriminate benign and malign tumour types in mammogram. PCA was used to reduce dimensionality. Finally, SVM was selected for classification. The author concluded that wavelet features seem to produce better discriminative power than features extracted by using PCA. Niwas et al. (Niwas et al., 2010) shed light on how the wavelet transform has become a valuable tool for numerous biomedical image and signal application tasks (Ma and Manjunath, 1995; Mabrouk et al., 2005; Selesnick et al., 2005). The author used the complex wavelet transform on nuclei objects of breast cancer images to extract textural features. Statistical

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textural features were then used to differentiate malignant samples from benign in cytological breast cancer images. The study demonstrated how DWT could be used to extract textural features. However, DWT could be used to extract colour characteristics of the nuclei under appropriate colour spaces.

Wavelet transform has the capability to decompose the signal to a number of coefficients, each represented by a vector. The vectors hold information that describes the coefficients at different levels. The high level coefficient holds global features, while the low level coefficient holds local features (Kokare et al., 2007, Serrano et al., 2004). Gupta et al. (Gupta et al., 2007) utilised the wavelets functionality to extract features from nuclear receptors. The features were extracted based on wavelet variance over the different levels of coefficient, which describes the seven important physicochemical properties of amino acids. In this study, wavelet transform has been used to analyse both grey scale and colour images. Chen et al. (Chen et al., 2001) used wavelet transform to extract grey-scale and grey-scale gradient features from grey scale cancer cell images, to aid in the diagnosis of lung cancer. The features are then fed into a neural network classifier to classify the samples. Ma (Ma, 2009) represented an approach for colour image retrieval based on HSV colour space and wavelet to extract features. The method works by building a dimensional feature vector that represents the colour features. Textural features based on wavelet transform were also extracted. Experimentation disclosed that texture features based on wavelet provided more effective results. This, and previously mentioned studies,

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demonstrates the discriminative power which wavelet features can provide. This has made DWT one of the most influential tools in providing discriminative features in nuclei cell classification tasks. However, the use of the appropriate decomposition levels play a vital role in the selection of the DWT coefficients, as these levels determine the quality of discriminative features provided by the DWT.

Another factor that could determine the performance of the extracted features is the type of classifier used. The use of Support Vector Machines (SVM) and artificial neural networks (ANN) is presented in the following section. These two types of classifier are reviewed in more detail as they are the primary classifiers used in the conducting of experiments in this thesis. Other classifiers are briefly mentioned in the next section.