Blood cancer is an umbrella term for cancers that affect the blood, bone marrow and lymphatic system. There are three main groups of blood cancer: leukemia, lymphoma and myeloma. Some types are more common than others. In this paper, a new image transform based on geometric mean properties of integral values in both horizontal and vertical image directions is proposed for leukemia cancer cell classification. Available classification methods using the classical feature extraction methods which are sensitive to rotation and deformation of the blood cells. The new transform is based on geometric mean projection, which -unlike other image transforms, such as Radon transform- is not considered all signals in an image with the same signal acquisition rate. Instead, it is general and thus applicable to all capturing signal functions to achieve sufficient invariant features. The geometric mean projection transforms (GMPT) guarantees that the detector only extracts the highly informative information from the object to achieve an invariant feature vector for an accurate classification process. This method has been used as cancer cell identification using microscopic Imagery analysis in this study. Dissimilarity metric calculation and shape analysis by using image transform has been used to extract the feature vectors of the imagery. Then, the accumulated feature vectors have been classified to different classes by using artificial neural network (ANN). The proposed technique has been evaluated in the standard images sourced from USIM, Malaysia. The evaluation results indicate the robustness of the technique in different types of images available in the dataset. � 2017 Universiti Putra Malaysia Press.

In this paper, we proposed an age assessment method evaluated on Malaysian children aged between 1 and 17. The approach is based on global fuzzy segmentation, local feature extraction using a projection-based feature transform and a designed deep convolutional neural networks (DCNNs) model. In the first step, a global labelling process was achieved based on fuzzy segmentation, and then, the first-to-third molar teeth were segmented. The deformation invariant features were next extracted based on an intensity projection technique. This technique provided high-order features which were invariant to rotation and partial deformation changes. Finally, the designed DCNN model extracts a large set of features in the hierarchical layers which provided scale, rotation and deformation invariance. The method using this approach was evaluated using a comprehensive and labelled orthopantomographs of 456 patients, which were captured in the Department of Dentistry and Research at Universiti Sains Islam Malaysia. The results from the analysis have suggested that the method can classify the images with high performance, which enabled automated age estimation with high accuracy. � 2019, Springer-Verlag London Ltd., part of Springer Nature.

Image registration of remotely sensed imagery is challenging, as complex deformations are common. Different deformations, such as affine and homogenous transformation, combined with multimodal data capturing can emerge in the data acquisition process. These effects, when combined, tend to compromise the performance of the currently available registration methods. A new image transform, known as geometric mean projection transform, is introduced in this work. As it is deformation invariant, it can be employed as a feature descriptor, whereby it analyzes the functions of all vertical and horizontal signals in local areas of the image. Moreover, an invariant feature correspondence method is proposed as a point matching algorithm, which incorporates new descriptor�s dissimilarity metric. Considering the image as a signal, the proposed approach utilizes a square Eigenvector correlation (SEC) based on the Eigenvector properties. In our experiments on standard test images sourced from �Featurespace� and �IKONOS� datasets, the proposed method achieved higher average accuracy relative to that obtained from other state of the art image registration techniques. The accuracy of the proposed method was assessed using six standard evaluation metrics. Furthermore, statistical analyses, including t-test and Friedman test, demonstrate that the method developed as a part of this study is superior to the existing methods. � 2018 Kahaki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.