Ms. Catalina Gómez Profile

Catalina Gómez

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 3 January 2020 Paper

Learning to segment brain tumors

Laura Daza, Catalina Gómez, Pablo Arbeláez

Proceedings Volume 11330, 113300G (2020) https://doi.org/10.1117/12.2542571

KEYWORDS: Image segmentation, Tumors, Brain, Computer programming, Medical imaging, Convolution, Diagnostics, Neuroimaging, 3D modeling, Image processing

Read Abstract +

Automatic segmentation of brain tumors is a challenging problem with many inherent difficulties, such as restricted training data, great intra-class variance, and volumetric images with large computational requirements in terms of processing. To overcome these difficulties, we propose Brain Tumor Parser (BTP), a novel convolutional neural network that takes advantage of a refinement module and global 3D information to perform semantic segmentation of brain structures in volumetric images with multiple modalities. We draw inspiration from recent breakthroughs in edge detection and semantic segmentation in natural images, and we build an accurate and effective three-dimensional network that segments small structures while refining large instances in multi-modal Magnetic Resonance Imaging (MRI). We evaluate our approach on the data from the Brain Tumor segmentation (BraTS) 2017 challenge, obtaining comparable results with the best performing algorithms, while using a single yet efficient architecture.

Proceedings Article | 17 November 2017 Paper

Recognition of skin melanoma through dermoscopic image analysis

Catalina Gómez, Diana Sofia Herrera

Proceedings Volume 10572, 1057211 (2017) https://doi.org/10.1117/12.2285957

KEYWORDS: Image segmentation, Skin, Melanoma, Algorithm development, Image classification, Skin cancer, Feature extraction, Image analysis

Read Abstract +

Melanoma skin cancer diagnosis can be challenging due to the similarities of the early stage symptoms with regular moles. Standardized visual parameters can be determined and characterized to suspect a melanoma cancer type. The automation of this diagnosis could have an impact in the medical field by providing a tool to support the specialists with high accuracy. The objective of this study is to develop an algorithm trained to distinguish a highly probable melanoma from a non-dangerous mole by the segmentation and classification of dermoscopic mole images. We evaluate our approach on the dataset provided by the International Skin Imaging Collaboration used in the International Challenge Skin Lesion Analysis Towards Melanoma Detection. For the segmentation task, we apply a preprocessing algorithm and use Otsu's thresholding in the best performing color space; the average Jaccard Index in the test dataset is 70.05%. For the subsequent classification stage, we use joint histograms in the YCbCr color space, a RBF Gaussian SVM trained with five features concerning circularity and irregularity of the segmented lesion, and the Gray Level Co-occurrence matrix features for texture analysis. These features are combined to obtain an Average Classification Accuracy of 63.3% in the test dataset.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years