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.

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.

Cloud computing is one of the most important technologies which supports reliability, scalability, ease of deployment and cost-efficient to business growth. Despite its benefits, cloud computing still has open and remain challenges on ensuring confidentiality, integrity, and availability (CIA) of sensitive data located on it. As a solution, the data is encrypted before sending to the cloud. However, the normal searching mechanism could not get through the encrypted data. In this paper, Searchable Encryption (SE) techniques which allow accessing data on encrypted cloud were reviewed. Nine SE techniques were presented with different issues and challenges on achieving secrecy and efficiency of SE. Four factors with their characteristics of SE were also identified for novice reader as a guidance of their future works.

Stroke is one of the main causes of disability in the world. In order for stroke survivors to reduce their disability, they need to go through a rehabilitation process to regain back their independence and improve their quality of life. To guide patients in their rehabilitation process and improve their receptiveness in performing repetitive exercises, a new rehabilitation training program using Virtual Reality (VR) technology has been introduced. This has attracted many researchers to explore more on VR technology as a new tool for stroke patient's rehabilitation. This paper presents a review on existing VR systems that have been developed for stroke rehabilitation. First, recent VR systems utilized for rehabilitation after stroke are delineated and categorized. Each of these categories concludes with a discussion on limitations and any issues that arise from it. Finally, a concise summary with significant findings and future possibilities in VR rehabilitation research is presented in table format.