The aim of this study is to review the methods being used for image analysis of microscopic views of immunohistochemically stained specimen in medical research. The solutions available range from general purpose software to commercial packages. Many studies have developed their own custom written programs based on some general purpose software available. Many groups have reported development of computer aided image analysis programs aiming at obtaining faster, simpler and cheaper solutions. Image analysis tools namely Aperio, Lucia, Metaview, Metamorph, ImageJ, Scion, Adobe Photoshop, Image Pro Plus are also used for evaluation of expressions using immunohistochemical staining. An overview of such methods used for image analysis is provided in this paper. This study concludes that there is good scope for development of freely available software for staining intensity quantification, which a medical researcher could easily use without requiring high level computer skills.

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An efficient detection of Optic disc in colour retinal images is the fundamental step in an automated retinal image analysis system. This paper presents a new approach for the automatic localization and accurate boundary detection of the optic disc. Iterative thresholding method followed by connected component analysis is employed to locate the approximate center of the optic disc. Then geometric model based implicit active contour model is applied to find the exact boundary of the optic disc. The method is evaluated against a carefully selected database of 148 retinal images and compared with the human expert. The optic disc is localized with an accuracy of 99.3%. The sensitivity and specificity of boundary detection achieved in terms of Mean±SD are 90.67±5 and 94.06±5 respectively.

This paper describes development of a decision support system for diagnosis of malaria using color image analysis. A hematologist has to study around 100 to 300 microscopic views of Giemsa-stained thin blood smear images to detect malaria parasites, evaluate the extent of infection and to identify the species of the parasite. The proposed algorithm picks up the suspicious regions and detects the parasites in images of all the views. The subimages representing all these parasites are put together to form a composite image which can be sent over a communication channel to obtain the opinion of a remote expert for accurate diagnosis and treatment. We demonstrate the use of the proposed technique for use as a decision support system by developing an android application which facilitates the communication with a remote expert for the final confirmation on the decision for treatment of malaria. Our algorithm detects around 96% of the parasites with a false positive rate of 20%. The Spearman correlation r was 0.88 with a confidence interval of 0.838 to 0.923, p<0.0001.

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In this paper, a general structure using dual-output operational transconductance amplifiers (OTAs) is explored to derive new first-order and second-order multi-function filters using OTAs with multiple current outputs and with and without grounded capacitors. Two first-order all-pass filters that have been obtained are presented, together with an analysis of nonidealities of OTAs, viz., finite bandwidth and parasitic output and input resistance and capacitance. Second-order multi-function filters obtained by using OTA-C simulated inductances in the proposed general structure are also presented. The realization of quadrature oscillator using the proposed first-order all-pass networks is also considered. Detailed analysis of nonideal frequency performance of the OTAs as well as finite output and input impedances is presented. SPICE simulation results are also given for the proposed circuits.

BackgroundImaging modalities in medicine gives complementary information. Inadequacy in clinical information made single imaging modality insufficient. There is a need for computer-based system that permits rapid acquisition of digital medical images and performs multi-modality registration, segmentation and three-dimensional planning of minimally invasive neurosurgical procedures. In this regard proposed article presents multimodal brain image registration and fusion for better neurosurgical planning.MethodsIn proposed work brain data is acquired from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) modalities. CT and MRI images are pre-processed and given for image registration. BSpline deformable registration and multiresolution image registration is performed on the CT and MRI sequence. CT is fixed image and MRI is moving image for registration. Later end result is fusion of CT and registered MRI sequences.ResultsBSpline deformable registration is performed on the slices gave promising results but on the sequences noise have been introduced in the resultant image because of multimodal and multiresolution input images. Then multiresolution registration technique is performed on the CT and MRI sequence of the brain which gave promising results.ConclusionThe end resultant fused images are validated by the radiologists and mutual information measure is used to validate registration results. It is found that CT and MRI sequence with more number of slices gave promising results. Few cases with deformation during misregistrations recorded with low mutual information of about 0.3 and which is not acceptable and few recorded with 0.6 and above mutual information during registration gives promising results.