Effect of Recombinant Tissue Plasminogen Activator and 120-kHz Ultrasound on Porcine Intracranial Thrombus Density.

Surgical intervention for the treatment of intracerebral hemorrhage (ICH) has been limited by inadequate lysis of the target thrombus. Adjuvant transcranial ultrasound exposure is hypothesized to improve thrombolysis, expedite hematoma evacuation and improve clinical outcomes. A juvenile porcine intracerebral hemorrhage model was established by direct infusion of autologous blood into the porcine white matter. Thrombi were either not treated (sham) or treated with recombinant tissue plasminogen activator alone (rt-PA only) or in combination with pulsed transcranial 120-kHz ultrasound (sonothrombolysis). After treatment, pigs were euthanized, the heads frozen and sectioned and the thrombi extracted. D-Dimer and thrombus density assays were used to assess degree of lysis. Both porcine and human D-dimer assays tested did not have sufficient sensitivity to detect porcine D-dimer. Thrombi treated with rt-PA with or without 120-kHz ultrasound had a significantly lower density compared with sham-treated thrombi. No enhancement of rt-PA-mediated thrombolysis was noted with the addition of 120-kHz ultrasound (sonothrombolysis). The thrombus density assay revealed thrombolytic efficacy caused by rt-PA in an in vivo juvenile porcine model of intracerebral hemorrhage. Transcranial sonothrombolysis did not enhance rt-PA-induced thrombolysis, likely because of the lack of exogenous cavitation nuclei.

An automated detection of glaucoma using histogram features.

Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of Glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns (LBP) and Daugman's algorithm are used to perform the feature set extraction. The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra (HOS) features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.