itk-segmentation

ITK is an open-source toolkit for multidimensional image analysis

Project description

ITK is an open-source, cross-platform library that provides developers with an extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and segmenting multidimensional scientific images.

This package addresses the segmentation problem: partition the image into classified regions (labels). This is a high level package that makes use of many lower level packages.

Project details

Release history Release notifications | RSS feed

5.2rc2 pre-release

Feb 12, 2021

5.2rc1 pre-release

Jan 6, 2021

5.1.2

Dec 2, 2020

5.1.1.post1

Oct 21, 2020

5.1.1

Aug 11, 2020

5.1.0.post3

Jun 15, 2020

5.1.0.post2

May 30, 2020

5.1.0.post1

May 26, 2020

5.1.0

Apr 27, 2020

5.1rc3 pre-release

Apr 2, 2020

5.1rc2 pre-release

Feb 21, 2020

5.1rc1.post1 pre-release

Dec 9, 2019

5.1rc1 pre-release

Dec 6, 2019

5.1b1 pre-release

Sep 4, 2019

5.0.1

Jul 24, 2019

5.0.0.post1

Jun 7, 2019

5.0.0

May 23, 2019

5.0rc2 pre-release

Apr 29, 2019

5.0rc1 pre-release

Feb 1, 2019

5.0b3 pre-release

Dec 21, 2018

5.0b1 pre-release

Sep 7, 2018

5.0a2 pre-release

May 25, 2018

5.0a1 pre-release

Apr 17, 2018

This version

4.13.2.post1

Mar 21, 2019

4.13.2

Mar 6, 2019

4.13.1.post1

Aug 11, 2018

4.13.0

Dec 21, 2017

4.12.0.post1

Jun 15, 2017

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for itk-segmentation, version 4.13.2.post1
Filename, size	File type	Python version	Upload date	Hashes
Filename, size itk_segmentation-4.13.2.post1-cp27-cp27m-macosx_10_9_x86_64.whl (5.7 MB)	File type Wheel	Python version cp27	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp27-cp27m-manylinux1_x86_64.whl (5.8 MB)	File type Wheel	Python version cp27	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp27-cp27mu-manylinux1_x86_64.whl (5.8 MB)	File type Wheel	Python version cp27	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp27-cp27m-win_amd64.whl (3.3 MB)	File type Wheel	Python version cp27	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp35-cp35m-macosx_10_9_x86_64.whl (5.7 MB)	File type Wheel	Python version cp35	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp35-cp35m-manylinux1_x86_64.whl (5.8 MB)	File type Wheel	Python version cp35	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp35-cp35m-win_amd64.whl (3.5 MB)	File type Wheel	Python version cp35	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp36-cp36m-macosx_10_9_x86_64.whl (5.7 MB)	File type Wheel	Python version cp36	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp36-cp36m-manylinux1_x86_64.whl (5.8 MB)	File type Wheel	Python version cp36	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp36-cp36m-win_amd64.whl (3.0 MB)	File type Wheel	Python version cp36	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp37-cp37m-macosx_10_9_x86_64.whl (5.7 MB)	File type Wheel	Python version cp37	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp37-cp37m-manylinux1_x86_64.whl (5.8 MB)	File type Wheel	Python version cp37	Upload date Mar 21, 2019	Hashes View
Filename, size itk_segmentation-4.13.2.post1-cp37-cp37m-win_amd64.whl (3.0 MB)	File type Wheel	Python version cp37	Upload date Mar 21, 2019	Hashes View

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27m-macosx_10_9_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`7e84d9d00bc8a06c1065772c69dccb6cb8945dfa9fc8c92d868ac269d35057d5`
MD5	`19491140738c17e535fdfb64c76d620d`
BLAKE2-256	`77447fe698a5154a43293762db1c78c2862e7cab2c1e7054fe0a407c37f62c62`

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27m-manylinux1_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`856bcf53287112b4067f1816ef8a31d1acfe3316ad19fba80fb48cf236bd3204`
MD5	`ff97e01c18ccd89324d95ff018f3824d`
BLAKE2-256	`75cb0aa64a0d5c55e9708a99450ca322872b61181d2272dd7a800d4cfd7ba58e`

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27mu-manylinux1_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`37cc17b2ecb6d0ffd11bfe846695590e2e8be368b136d8a79245d7d235d82b1c`
MD5	`f613c05553d18d62c0c6035a49a32049`
BLAKE2-256	`73ac908dc1277a3f850615f1783d24102cb40aa812fc048392fb2670de1d5509`

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27m-win_amd64.whl

Hashes for itk_segmentation-4.13.2.post1-cp27-cp27m-win_amd64.whl
Algorithm	Hash digest
SHA256	`00fb04f5241353948194e8d7b637ac4bc7f9878fe5e7906e165dfab138044b7a`
MD5	`1b734491f45b1ef85884dfd805dfde9d`
BLAKE2-256	`f252c7a2a958867eaa27365c32d4d800a28606303d0323e8fe7b419c220bf124`

Hashes for itk_segmentation-4.13.2.post1-cp35-cp35m-macosx_10_9_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp35-cp35m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`24c656a97530bece12b51f2484d8e318a3a43a301b92c2fa577b586492b10b9f`
MD5	`cc98c93d2f4627e5657f99d5606a7b81`
BLAKE2-256	`63c4e58ab539a732e0e27b705cd076c06ef638dc4b0dd99203a10e6c97747e0c`

Hashes for itk_segmentation-4.13.2.post1-cp35-cp35m-manylinux1_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp35-cp35m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`15cd8c4635d7e5f789df456372a25bba9dca3f392ad19b1e18789ee2d9db095a`
MD5	`8fd65160c0f8a1d1008ba2d2f3edecf8`
BLAKE2-256	`3746f6d4a41a10948c546c8e6458f8c00204a7dd58955c66014f982f25e4f387`

Hashes for itk_segmentation-4.13.2.post1-cp35-cp35m-win_amd64.whl

Hashes for itk_segmentation-4.13.2.post1-cp35-cp35m-win_amd64.whl
Algorithm	Hash digest
SHA256	`0c9728c73ee2cc6f9b56c13c3869fce064207454315dbed10c8af2cfb2fab266`
MD5	`c81e8e28bf69a808051b9dc2b83e93ca`
BLAKE2-256	`dd64629781a05b4c621ba10cd7b45483070442d7897ebdcfb9fbf0dd0cdb24cd`

Hashes for itk_segmentation-4.13.2.post1-cp36-cp36m-macosx_10_9_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`01f55cf07dd1f7a1bab0618683c4bd7460c7c5e9e453b2530ba1d586a6f7cf9e`
MD5	`e29a8643b760873c9efa750e93b3b2f3`
BLAKE2-256	`a0bb4b1124b13c8aa4f55249a08c7226ad0442219a8b2987f345e6a189c4f479`

Hashes for itk_segmentation-4.13.2.post1-cp36-cp36m-manylinux1_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp36-cp36m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`7d2873f0dd5d76ea000af0410f513098e7471fc4da41b1139f490c0338d538af`
MD5	`94e67ec3d7cf61ac5fda981a26d5cef4`
BLAKE2-256	`6851ebb239fe03b99fadb88ab1bbf9a7b13ea865f1d50fc333bc9222b4dc41bf`

Hashes for itk_segmentation-4.13.2.post1-cp36-cp36m-win_amd64.whl

Hashes for itk_segmentation-4.13.2.post1-cp36-cp36m-win_amd64.whl
Algorithm	Hash digest
SHA256	`1876093ac915cd475fbda562238fc3f50ff9f532278154f5b9269a14e99f110d`
MD5	`c1046756b04bb55bf191d3a4d31342ad`
BLAKE2-256	`aa21759afc6c9524d3a68b9853d46b9b013f07c92cdc0fc0a181ed142f152491`

Hashes for itk_segmentation-4.13.2.post1-cp37-cp37m-macosx_10_9_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm	Hash digest
SHA256	`ecef76e3f648c40d216bc2ac6262f3eb5f1709a0a6a446ff41327f0ac7cd9be9`
MD5	`b0ab5645b5d6681f4a49f949fff55303`
BLAKE2-256	`f70d7a1e19276ca4fed5bbdf83da7f931e634e71a19f5d12a454744f48965891`

Hashes for itk_segmentation-4.13.2.post1-cp37-cp37m-manylinux1_x86_64.whl

Hashes for itk_segmentation-4.13.2.post1-cp37-cp37m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`cc862cc8dd416dd0eb16411a101ff5faca28e446b1eaafd5a993ad68606d6331`
MD5	`9eaf17219f1a01b7ed2cbebf0f85cf0f`
BLAKE2-256	`0e5de15bf77513f46dfbb0d479b7e102203c2180192596dc989ec6c8551c6e4f`

Hashes for itk_segmentation-4.13.2.post1-cp37-cp37m-win_amd64.whl

Hashes for itk_segmentation-4.13.2.post1-cp37-cp37m-win_amd64.whl
Algorithm	Hash digest
SHA256	`90c8150f29297515aeda17c6a6b16bbbc149f30989891b87832aaf945d7395d4`
MD5	`95124e02ef52804d5b0edf2eed617233`
BLAKE2-256	`da32b9c06c258fc86580a36d07a94ebf3ae2280b1a4472e952c03dacc620c6bd`