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Optimas Image Analysis Software Free Download ((FULL))


Optimas Image Analysis Software Free Download > https://blltly.com/2trQZa



Optimas Image Analysis Software Free Download ((FULL))


OPTIMAS offers hundreds of powerful image processing and measurement functions, seamless output to ASCII files or Excel worksheets, a powerful programming environment with automatic scripting, and compatibility with industry standard capture hardware and cameras for monochrome and colour images. OPTIMAS also supports high bit depth images, image sequence analysis, advanced morphology, and Microsoft Visual Basic compatibility. Operating as a native 32-bit application, OPTIMAS takes full advantage of the processing capabilities of 32-bit operating systems such as Windows 95 and Windows NT.


Out of sheer necessity to analyze a massive image set before I turned grey, I scrounged around for free open source software programs to help analyze my confocal microscopy image stacks.


It can do simple things like crop, label, and alter the brightness and contrast of fluorescence images. It can also easily handle 3D stacks of confocal microscopy images, and perform complex quantitative analysis.


VIAS enables you to tile multiple confocal microscopy image stacks into a single 3D image dataset. This is a software program made by the same group that created Neuronstudio. It has a similar intuitive user interface and a step-by-step online guide.


As we are not responsible for creating any of this software, I can only speak from my experience with these programs. I have used Micro-manager ( -manager.org/) in the past on an Olympus microscope (not sure what the camera was) to acquire images. It was super simple to use. I would recommend you checkout the list of acquisition plugins on the ImageJ website ( ). You may be able to find something that meets your needs.


Features:Interactive, easy-to-use graphical interfaceOutputs a stack of deconvolved imagesNearest-neighbor and single image clean-upManual 24 bit (RGB) true color deconvolved imagesIntegrated macro and powerful scripting functionsAccepts 8 bit grayscale Applications:General clean up of hazy imagesImage mode: fluorescent, transmitted, reflected, DIC, etc.Ratio imagingImprove images captured with a confocal microscope Requirements:Additional software driver for framegrabber may be requiredIBM PC 486 or Pentium, 500 Mb hard disk32 Mb of RAM (larger data sets may require additional RAM)Optional: will work with Image Pro Plus, Optimas, IPLab or is available in STAND ALONE modeVayTek, Inc., 305 West Lowe Avenue, Fairfield, IA 52556. Tel: 515-472-2227; Fax: 641-472-8131.


Computerized image analysis (IA) system has emerged in recent years as a very powerful tool for objective and reproducible quantification of histological features. It has shown considerable potential for diagnostic application in diverse histological situations. The objectives of the present study were to evaluate the discriminatory diagnostic efficiency of computerized image analysis based quantitative subvisual nuclear parameters in papillary and follicular neoplasms of thyroid. A total of 60 cases were studied. Forty-four cases belonged to training set and 16 cases belonged to a test set. A minimum of 100 nuclei was analyzed in each case using uniform 5 μm thick hematoxylin stained sections. The IA workstation comprised of an Olympus microscope, a 10 bit digital video camera, an image grabber card and a pentium 120 MHz computer. Optimas 5.2 software was utilized for data collection on 8 morphometric and 8 densitometric parameters. Multivariate stepwise discriminant statistical analysis of data was done with the help of BMDP statistical software release 7.0. Results from a training set revealed correct classification rates of 98.0%, 84.5% and 61.2% for the histological groups of hyperplastic papillae versus papillae of papillary carcinoma (group I), follicular variant of papillary carcinoma versus the broad category of follicular neoplasms consisting of both follicular adenoma and follicular carcinoma (group II) and follicular adenoma versus follicular carcinoma (gr




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