Reading group - 22/05/2013
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Slides from the reading group presentation where I introduced a new Python interface for IRTK. See http://kevin-keraudren.blogspot.co.uk/2013/12/irtk-python.html for more details and the iPython notebook demo.
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Parallel computing is a type of computation in which many processing are performed concurrently often by dividing large problems into smaller ones that execute independently of each other. There are several different types of parallel computing. The first one is the shared memory architecture which harnesses the power of multiple processors and multiple cores on a single machine and uses threads of programs and shared memory to exchange data. The second type of parallel computing is the distributed architecture which harnesses the power of multiple machines in a networked environment and uses message passing to communicate processes actions to one another. This paper implements several computational algorithms using parallel programming techniques namely distributed message passing. The algorithms are Mandelbrot set, Bucket Sort, Monte Carlo, Grayscale Image Transformation, Array Summation, and Insertion Sort algorithms. All these algorithms are to be implemented using C .NET and tested in a parallel environment using the MPI.NET SDK and the DeinoMPI API. Experiments conducted showed that the proposed parallel algorithms have faster execution time than their sequential counterparts. As future work, the proposed algorithms are to be redesigned to operate on shared memory multi processor and multi core architectures. Youssef Bassil ""Implementation of Computational Algorithms using Parallel Programming"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22947.pdf
Paper URL: https://www.ijtsrd.com/computer-science/parallel-computing/22947/implementation-of-computational-algorithms-using-parallel-programming/youssef-bassil
Analytics with Spark
The document discusses analytics for sensor data from the Internet of Things. It provides examples of using sensor data from aircraft and connected cars for applications like optimizing flight performance, detecting anomalies, and monitoring vehicle location and driving habits. It then describes collecting accelerometer data from mobile devices, analyzing the data with Apache Spark and MLlib to identify physical activities, and storing the data in Cassandra. Algorithms like decision trees, random forests, and logistic regression are used to build predictive models to classify activities in real-time.
Improving Android Performance at Mobiconf 2014
The document provides an overview of improving Android performance by discussing Java virtual machines, examples of code optimizations and anti-patterns, and tools for measuring and optimizing performance. It covers topics like avoiding autoboxing when possible, using arrays over lists for loops, caching array lengths, and using StringBuilder instead of string concatenation. Tooling discussed includes using a disassembler to view bytecode, code obfuscation with Proguard, and best practices for performance measurement.
Clustbigfim frequent itemset mining of
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans
algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
COSC 426 Lect. 3 -AR Developer Tools
This document discusses augmented reality (AR) developer tools. It describes several low-level AR libraries including ARToolKit, FLARToolKit, and SSTT. It then discusses additional AR authoring software like osgART, Studierstube, MXRToolKit, DART, mARx, AMIRE, ComposAR, and iaTAR that provide higher-level tools for building AR applications and experiences. The document also covers components of AR applications like tracking, display, and some example ARToolKit applications.
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...
This document describes the ClustBigFIM algorithm for frequent itemset mining of big data using pre-processing based on the MapReduce framework. The ClustBigFIM algorithm first applies k-means clustering to generate clusters from large datasets. It then mines frequent itemsets from the generated clusters using the Apriori and Eclat algorithms within the MapReduce programming model. Experimental results on several datasets show that the ClustBigFIM algorithm increases execution efficiency compared to the BigFIM algorithm by applying k-means clustering as a pre-processing step before frequent itemset mining.
Report
This document appears to be a thesis submitted by Conor McMenamin for their B.Sc. in Computational Thinking at Maynooth University. The thesis investigates existing standards for selecting elliptic curves for use in elliptic curve cryptography (ECC) and whether it is possible to manipulate the standards to exploit weaknesses. It provides background on elliptic curve theory, cryptography, and standards. The document outlines requirements and proposes designing a system to test manipulating the standards by choosing curves with a user-selected parameter ("BADA55") to simulate exploiting a weakness. It describes implementing and testing the system before concluding and discussing future work.
How to add an optimization for C# to RyuJIT
This document discusses various ways to optimize C# code by adding custom optimizations to the JIT compiler. It begins by explaining how to morph the intermediate representation (IR) tree during JIT compilation to optimize expressions like dividing by a constant. It then covers implementing range check elimination, loop optimizations like invariant code hoisting, and ideas for optimizations not yet implemented like loop unrolling and deletion. The goal is to explore how to most easily add optimizations by modifying phases in the JIT compiler.
PVS-Studio 5.00, a solution for developers of modern resource-intensive appl...
PVS-Studio is a static analyzer that detects errors in source code of C, C++, C++11, C++/CX applications.
Efficient SIMD Vectorization for Hashing in OpenCL
This document presents research on improving the performance of hashing operations through vectorization using OpenCL. Vectorized hashing primitives implemented with OpenCL were shown to outperform scalar implementations on Xeon CPUs, providing portability across processors. Processor-specific intrinsics provide even higher performance than OpenCL, especially on Xeon Phi processors. The goal of the research is to develop portable vectorized hashing primitives in OpenCL to improve database operator performance while maintaining readable and maintainable code.
Adam Sitnik "State of the .NET Performance"
MSK DOT NET #5
2016-12-07
In this talk Adam will describe how latest changes in.NET are affecting performance.
Adam wants to go through:
C# 7: ref locals and ref returns, ValueTuples.
.NET Core: Spans, Buffers, ValueTasks
And how all of these things help build zero-copy streams aka Channels/Pipelines which are going to be a game changer in the next year.
State of the .Net Performance
This document summarizes Adam Sitnik's presentation on .NET performance. It discusses new features in C# 7 like ValueTuple, ref returns and locals, and Span. It also covers .NET Core improvements such as ArrayPool and ValueTask that reduce allocations. The presentation shows how these features improve performance through benchmarks and reduces GC pressure. It provides examples and guidance on best using new features like Span, pipelines, and unsafe code.
How to Make Hand Detector on Native Activity with OpenCV
How to Make Hand Detector on Native Activity with OpenCVIndustrial Technology Research Institute (ITRI)(工業技術研究院, 工研院)
This document provides an overview and instructions for making a hand detector on Android using OpenCV. It discusses calculating a skin color histogram, detecting skin areas in an image, finding the largest skin area, and matching histograms using Histograms of Oriented Gradients (HoG). Steps include converting to HSV, generating a histogram in H-S space, applying the histogram to detect skin pixels, filtering and finding contours, labeling connected areas, and getting the largest. HoG extracts gradient features from blocks for matching.LSFMM 2019 BPF Observability
Here is a bpftrace program to measure scheduler latency for ICMP echo requests:
#!/usr/local/bin/bpftrace
kprobe:icmp_send {
@start[tid] = nsecs;
}
kprobe:__netif_receive_skb_core {
@diff[tid] = hist(nsecs - @start[tid]);
delete(@start[tid]);
}
END {
print(@diff);
clear(@diff);
}
This traces the time between the icmp_send kernel function (when the packet is queued for transmit) and the __netif_receive_skb_core function (when the response packet is received). The
A Multiple Kernel Learning Based Fusion Framework for Real-Time Multi-View Ac...
A Multiple Kernel Learning Based Fusion Framework for Real-Time Multi-View Ac...Francisco (Paco) Florez-Revuelta
Paper presented at the 6th International Work-Conference on Ambient Assisted Living.
Abstract: Due to the increasing demand of multi-camera setup and long-term monitoring in vision applications, real-time multi-view action recognition has gain a great interest in recent years. In this paper, we propose a multiple kernel learning based fusion framework that employs a motion-based person detector for finding regions of interest and local descriptors with bag-of-words quantisation for feature representation. The experimental results on a multi-view action dataset suggest that the proposed framework significantly outperforms simple fusion techniques and state-of-the-art methods.Similar to Reading group - 22/05/2013 (20)
Stranger in These Parts. A Hired Gun in the JS Corral (JSConf US 2012)
Stranger in These Parts. A Hired Gun in the JS Corral (JSConf US 2012)
Using FPGA Design and HIL Algorithm Simulation to Control Visual Servoing
Using FPGA Design and HIL Algorithm Simulation to Control Visual Servoing
Introduction to Monte Carlo Ray Tracing, OpenCL Implementation (CEDEC 2014)
Introduction to Monte Carlo Ray Tracing, OpenCL Implementation (CEDEC 2014)
Implementation of Computational Algorithms using Parallel Programming
Implementation of Computational Algorithms using Parallel Programming
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...
PVS-Studio 5.00, a solution for developers of modern resource-intensive appl...
PVS-Studio 5.00, a solution for developers of modern resource-intensive appl...
Efficient SIMD Vectorization for Hashing in OpenCL
Efficient SIMD Vectorization for Hashing in OpenCL
How to Make Hand Detector on Native Activity with OpenCV
How to Make Hand Detector on Native Activity with OpenCV
A Multiple Kernel Learning Based Fusion Framework for Real-Time Multi-View Ac...
A Multiple Kernel Learning Based Fusion Framework for Real-Time Multi-View Ac...
More from Kevin Keraudren
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013 poster)
This document presents a method for automatically localizing the fetal brain in MRI scans acquired as stacks of misaligned 2D slices due to fetal motion. The method first detects Maximally Stable Extremal Regions in each slice and filters by size. Histograms of SIFT features from the regions are classified using SVM to identify the brain. A 3D bounding box is fitted using RANSAC. Evaluation on 59 fetuses showed the detected box contained the entire brain in 85% of cases with a median error of 5.7mm from ground truth.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction (...
This document proposes an automated method for segmenting the fetal brain from 2D MRI slices that have been misaligned due to fetal motion. It combines fetal brain localization in each slice using Maximally Stable Extremal Regions (MSER) and Scale-Invariant Feature Transform (SIFT) features with a patch-based propagation and Conditional Random Field (CRF) to generate a segmentation mask for each slice. It then integrates this slice-by-slice segmentation with a motion correction process to iteratively refine the segmentation as the reconstruction proceeds. The method was tested on 66 datasets ranging from 22-39 weeks gestation and produced a motion corrected volume of diagnostic quality in 85% of cases while also generating a mean 93
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
The proposed method uses random forests with steerable features to automatically localize fetal organs (heart, lungs, liver) in MRI. During training, images are mapped to a standard coordinate system defined by anatomical landmarks and normalized for fetal age. At testing, features are extracted in rotating coordinate systems to account for the fetus' unpredictable orientation. The method was tested on healthy fetuses and fetuses with IUGR, achieving over 90% detection rates for healthy fetuses without motion artifacts, and 83%, 78%, 67% detection rates for heart, lungs, liver respectively in the presence of motion. The method can initialize segmentation and motion correction and automatically orient volumes based on fetal anatomy.
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
This document presents a method for automatically localizing fetal organs in MRI scans using random forests with steerable features. The method first normalizes fetal size, then uses a classification and regression pipeline with random forests to assign voxels to organs and vote for organ centers. Features are steered based on detected landmarks like the brain to account for unknown fetal orientation. Evaluation on two datasets found the heart was localized within 10mm of ground truth in 90% of cases, suggesting it could initialize motion correction. Future work will use the detections for slice-by-slice segmentation to improve motion correction quality.
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013)
Localization of the fetal brain in MRI poses challenges due to fetal motion during image acquisition. The authors propose a 2D detection method using Maximally Stable Extremal Regions and bundled SIFT features to classify regions as brain or non-brain. A RANSAC procedure then fits an axis-aligned 3D box to the detected regions. On a dataset of 59 fetuses, the method obtained a median error of 5.7mm from ground truth with no missed detections, outperforming alternatives using 2D or 3D SIFT features. The prior knowledge of fetal brain size based on gestational age improves robustness to motion artifacts.
Segmenting Epithelial Cells in High-Throughput RNAi Screens (Miaab 2011)
This document summarizes a proposed method for segmenting epithelial cells in high-throughput RNAi screens using image analysis. The method uses a pipeline that includes pre-processing images using filters to reduce noise and enhance cell structures, segmenting nuclei, generating an edge map of cell-cell contacts, and performing an adaptive watershed segmentation to extract three structures: cell-cell contacts, nuclei, and cell walls. The method is shown to accurately segment these structures and provide reliable quantification of markers in different experimental conditions, distinguishing effects of depleting different actin-binding proteins on cell-cell adhesion receptors and the cytoskeleton.
Keraudren-K-2015-PhD-Thesis
This thesis presents methods for the automated localisation of organs in fetal magnetic resonance imaging (MRI) to enable automated preprocessing for motion correction. The first method localises the fetal brain independently of orientation using a Viola-Jones detector followed by classification of image regions with bundled SIFT features. This localisation of the brain is then used to steer the localisation of the heart, lungs and liver using segmentation with autocontext random forests and random forests with steerable features. Evaluation shows the brain localisation and segmentation performs as well as manual preprocessing. Preliminary results on motion correction of the fetal thorax using the heart, lung and liver localisation are also presented.
PhD viva - 11th November 2015
This thesis presents methods for automatically localizing fetal organs in MRI scans. It describes localizing the brain in 2 steps - detecting candidate brain regions using size filtering then further localizing through slice-by-slice segmentation, achieving median error of 5.7mm. For the body, it sequentially localizes organs by normalizing size by gestational age and using steerable image features informed by anatomy, detecting the heart center within 10mm in 90% of cases. This allows fully automated motion correction in over 70% of scans, presenting the first method to fully automatically localize multiple fetal organs beyond just the brain.
PyData London 2015 - Localising Organs of the Fetus in MRI Data Using Python
This document summarizes an automated method for localizing fetal organs in magnetic resonance images. The method uses machine learning to sequentially localize the brain, heart, lungs and liver. It first normalizes fetal size based on gestational age. It then localizes the brain, uses this to search for the heart between two spheres. The heart location guides searching inside a third sphere for the lungs and liver. Features incorporate spatial relationships modeled by Gaussian distributions. Classification predicts organ candidates, regression refines locations, and spatial optimization selects the final detection by maximizing votes and relative organ positions. Training involves extracting random cube features around labeled pixels to classify organs.
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction
This document describes a method for automated fetal brain segmentation from 2D MRI slices in order to perform motion correction. The method uses box detection algorithms like MSER and SIFT to detect the brain region in each slice. It then trains a random forest classifier on brain and non-brain patches to perform brain extraction. Finally, it uses a conditional random field for motion correction across slices to generate a 3D volume with less artifacts from fetal movement. The results showed the proposed method produced motion-corrected volumes of diagnostic quality in 85% of test cases.
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiograph...
Slides from Ozan Oktay at the MICCAI workshop on Sparsity Techniques in Medical Imaging (STMI2014), presenting one of the methods we used in the CETUS challenge (http://www.creatis.insa-lyon.fr/Challenge/CETUS/index.html).
Endocardial 3D Ultrasound Segmentation using Autocontext Random ForestsPresen...
The document describes an autocontext random forest approach for endocardial 3D ultrasound segmentation. It uses successive random forest classifiers, where each gains contextual information from the previous ones. The first classifier defines the centers for the left ventricle, myocardium, and mitral valve. Subsequent classifiers perform tests on the input image, current probability maps, and geodesic distance maps. The tests compare mean intensities between offset patches from these sources. The implementation uses 4 iterations of autocontext with random forests of 20 trees and a maximal depth of 20.
Faceccrumbs: Manifold Learning on 1M Face Images, MSc group project
MSc group project (March 2011) at Imperial College which emulated Google's People Hopper:
http://googleresearch.blogspot.co.uk/2010/03/hopping-on-face-manifold-via-people.html
Slides on Photosynth.net, from my MSc at Imperial
The document discusses 3D browsing of photo datasets using Photosynth.net. It describes the Bundler pipeline which involves extracting focal lengths from photos, finding feature points using SIFT, matching descriptors between photos, and using structure from motion to recover camera parameters and 3D point locations. It also discusses rendering the 3D scene and exploring it. Key steps include finding matches using approximate nearest neighbors and RANSAC, organizing matches into tracks, and incremental bundle adjustment to refine the model.
Slides presented at the Steiner Unit, Hammersmith Hospital, 08/06/2012
Kevin Keraudren started a PhD in 2011 on organ localization in fetal MRI. His own research includes developing a 2D detector for fetal heads in MRI images and detecting eyes. Future plans are to validate current detectors, target other organs, and improve visualization of fetal MRI data through semi-automatic organ cataloguing.
Introduction to cython: example of GCoptimization
This document discusses using Cython to interface Python with C/C++ code to improve computational performance. It provides two examples: (1) wrapping an entire C++ graph cut library in Cython, resulting in an 18 second runtime; and (2) using Cython to call a C++ graph cut function as a black box, achieving a runtime of 0.37 seconds, nearly 50 times faster. The document emphasizes that Cython can provide large speedups with relatively little code by leveraging existing optimized C/C++ implementations.
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
Slides presented at the workshop in Microscopic Image Analysis with Applications in Biology, Heidelberg, September 2011. The associated paper can be found here: http://www.doc.ic.ac.uk/~kpk09/publications/MIAAB-2011.pdf
More from Kevin Keraudren (17)
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013 poster)
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013 poster)
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction (...
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction (...
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013)
Automatic Localisation of the Brain in Fetal MRI (Miccai 2013)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (Miaab 2011)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (Miaab 2011)
PyData London 2015 - Localising Organs of the Fetus in MRI Data Using Python
PyData London 2015 - Localising Organs of the Fetus in MRI Data Using Python
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction
Automated Fetal Brain Segmentation from 2D MRI Slices for Motion Correction
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiograph...
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiograph...
Endocardial 3D Ultrasound Segmentation using Autocontext Random ForestsPresen...
Endocardial 3D Ultrasound Segmentation using Autocontext Random ForestsPresen...
Faceccrumbs: Manifold Learning on 1M Face Images, MSc group project
Faceccrumbs: Manifold Learning on 1M Face Images, MSc group project
Slides presented at the Steiner Unit, Hammersmith Hospital, 08/06/2012
Slides presented at the Steiner Unit, Hammersmith Hospital, 08/06/2012
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
Segmenting Epithelial Cells in High-Throughput RNAi Screens (MIAAB 2011)
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What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
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Microsoft - Power Platform_G.Aspiotis.pdf
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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Video Streaming: Then, Now, and in the Future
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“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Reading group - 22/05/2013
- 1. Organ Detection in Fetal MRI Kevin Keraudren Imperial College London May 22nd , 2013
- 2. 1 Overview 2 Python interface for IRTK 3 Future work 2/27
- 3. 1 Overview 2 Python interface for IRTK 3 Future work 3/27
- 4. Overview 1 Brain detection & reconstruction 2 3D visualisation 3 Organ localisation 4/27
- 5. People Brain reconstruction: Maria Murgasova 3D visualisation: Christina Malamateniou Organ detection: Bernhard Kainz 5/27
- 6. Localisation of the Brain in Fetal MRI Using Bundled SIFT Features For every slice Detect MSER regions Filter by size RANSAC Classify using SIFT features 6/27
- 7. Detections results averaged over the cross validation (all orientations combined) Centiles 25th 50th 75th 2D SIFT 10.9 15.5 20.5 Error (mm) 3D SIFT 14.8 20.8 30.4 Bundled SIFT 4.0 5.7 8.4 Detection Complete brain 98% 38% 85% 23% 100% 85% 7/27
- 8. 1 Overview 2 Python interface for IRTK 3 Future work 8/27
- 9. Why Python? ls *.nii | python -c ’import sys, irtk; [sys.stdout.write( str(irtk.imread(line.rstrip(), dtype="float32").max())+"n") for line in sys.stdin]’ numpy, matplotlib, OpenCV, VTK, scipy.ndimage... & C++ via cython 9/27
- 10. Why another library? import itk pixelType = itk.UC imageType = itk.Image[pixelType, 3] readerType = itk.ImageFileReader[imageType] writerType = itk.ImageFileWriter[imageType] reader = readerType.New() reader.SetFileName( "input.nii" ) reader.Update() itk2np = itk.PyBuffer[imageType] data = itk2np.GetArrayFromImage( reader.GetOutput() ) ... 10/27
- 11. Why another library? import SimpleITK as sitk img = sitk.ReadImage( "input.nii" ) data = sitk.GetArrayFromImage( img ) ... output = sitk.GetImageFromArray( data ) output.SetSpacing( img.GetSpacing() ) output.SetOrigin( img.GetOrigin() ) output.SetDirection( img.GetDirection() ) sitk.WriteImage( output, "output.nii" ) 11/27
- 12. Why another library? import irtk img = irtk.imread( "input.nii" ) ... irtk.imwrite( "output.nii", img ) 12/27
- 13. Key features simple and pythonic overload getitem instead of GetRegion parallelisation using joblib visualisation functions 13/27
- 14. Implementation template <class dtype> void irtk2py( irtkGenericImage<dtype>& irtk_image, dtype* img, double* pixelSize, double* xAxis, double* yAxis, double* zAxis, double* origin, int* dim ); 14/27
- 15. Implementation void py2rigid( irtkRigidTransformation &transform, double tx, double ty, double tz, double rx, double ry, double rz, bool invert=false ); 15/27
- 17. Parallelisation example import irtk from joblib import Parallel, delayed from glob import glob def register( f, img1): img2 = irtk.imread( f, dtype=’float32’) img2 = img2.rescale().resample(2) t = img2.register(img1) return t filenames = glob( raw_folder + "/" + patient_id + "_*.nii") img1 = irtk.imread(filenames[0], dtype=’float32’) img1 = img1.rescale().resample(2) transformations = [irtk.RigidTransformation()] transformations.extend( Parallel(n_jobs=-1)(delayed(register)(f, img1) for f in filenames[1:] ) ) 17/27
- 18. 1 Overview 2 Python interface for IRTK 3 Future work 18/27
- 19. Coarse segmentation as side product of detection 19/27
- 20. Convex hull of all detections 20/27
- 24. Room for improvement Refine motion model (statistics on slice transformations) Patch based segmentation instead of graphcut Detect head orientation to solve more difficult registrations Could the detection process produce more than a mask? 24/27
- 25. Could we reconstruct more than the brain? 25/27
- 26. For the future (hanging projects) Spine detection using SLIC supervoxels Dense volumetric features: SURF 3D Alignment of mothers’ bodies to model of a pregnant woman 26/27