Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain
Home > Computer & Internet > Computer science > Mathematical theory of computation > Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain
Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain

Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain


     0     
5
4
3
2
1



International Edition


About the Book

In this book the fully automatic generation of semantic annotations for medical imaging data by means of medical image segmentation and labeling is addressed. In particular, the focus is on the segmentation of the human brain and related structures from magnetic resonance imaging (MRI) data. Three novel probabilistic methods from the field of database-guided knowledge-based medical image segmentation are presented. Each of the methods is applied to one of three MRI segmentation scenarios: 1) 3-D MRI brain tissue classification and intensity non-uniformity correction, 2) pediatric brain cancer segmentation in multi-spectral 3-D MRI, and 3) 3-D MRI anatomical brain structure segmentation. All the newly developed methods make use of domain knowledge encoded by probabilistic boosting-trees (PBT), which is a recent machine learning technique. For all the methods uniform probabilistic formalisms are presented that group the methods into the broader context of probabilistic modeling for the purpose of image segmentation. It is shown by comparison with other methods from the literature that in all the scenarios the newly developed algorithms in most cases give more accurate results and have a lower computational cost. Evaluation on publicly available benchmarking data sets ensures reliable comparability of the results to those of other current and future methods. One of the methods successfully participated in the ongoing online caudate segmentation challenge (www.cause07.org), where it ranks among the top five methods for this particular segmentation scenario.


Best Sellers



Product Details
  • ISBN-13: 9783832526313
  • Publisher: Logos Verlag Berlin
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 0 mm
  • Width: 145 mm
  • ISBN-10: 3832526315
  • Publisher Date: 15 Oct 2010
  • Height: 210 mm
  • No of Pages: 148
  • Series Title: Studien Zur Mustererkennung
  • Weight: 750 gr


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain
Logos Verlag Berlin -
Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals



    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!