andrefnb/cell_image_clustering

Feature extraction and unsupervised learning for cell image dataset.

cell_image_clustering

Master's Degree in Big Data analysis and engeneering. Assignment 2 of the Machine Learning course.

Objective

The goal of this assignment is to examine a set of bacterial cell images using machine learning techniques, including feature extraction, features selection and clustering, in order to help the biologists organize similar images. In this zip file, tp2.zip, you have a set of 563 PNG images (in the images/ folder) taken from a super-resolution fluorescence microscopy photograph of Staphylococcus aureus, a common cause of hospital infections and often resistant to multiple antibiotics.

The images provided for this assignment were obtained by automatic segmentation and include cells in different stages of their life cicle as well as segmentation errors, not corresponding to real cells.

All images have the same dimensions, 50 by 50 pixels, with a black background and the segmented region centered in the image. In this assignment, you will load all images, extract features, examine them and select a subset for clustering with the goal of reaching some conclusion about the best way of grouping these images.

Implementation

In the tp2.zip file provided there is a Python module, tp2_aux.py, with a function, images_as_matrix(), that returns a 2D numpy array with one image per row (563 rows) and one pixel per column (50x50=2500 columns) from the images in the images folder.

From this matrix, you will extract features using three different methods:

Principal Component Analysis (PCA) Use the PCA class from the sklearn.decomposition module. t-Distributed Stochastic Neighbor Embedding (t-SNE) Use the TSNE class from the sklearn.manifold module. When creating an object of this class, use the method='exact' argument, for otherwise the TSNE constructor will use a faster, approximate, computation which allows for at most 3 components. Isometric mapping with Isomap Use the Isomap class from the sklearn.manifold module.

With each method, extract six features from the data set, for a total of 18 features.

In addition to the images, the labels.txt has information on the identification of the cell cycle phase for each cell. The first column has the cell identifier and the second column a label for the cell cycle phase. These cells were manually labelled by biologists. The figure below illustrates examples from the 3 phases, labelled with integers 1, 2 and 3. The first phase before the cell starts to divide, the second covers the first part of the division, with the formation of a membrane ring where the cells will divide, and the third phase corresponds to the final stage of cell division. However, note that only some images are labelled. Images that were not labelled have a label value of 0 in this file.

After extracting features, select the best for clustering.

For this assignment, you will parametrize and compare at least two clustering algorithms: DBSCAN and K-Means. FIrst, for the DBSCAN algorithm, read the original paper on this algorithm and implement the parameter selection method described:

A density-based algorithm for discovering clusters in large spatial databases with noise (1996). Martin Ester , Hans-Peter Kriegel , Jörg Sander , Xiaowei Xu. The paper also available here.

The authors suggest a value of 4 for the minimum number of neighbouring points required for a point to be considered a core point. However, you should use a value of 5 as this is the default value in the Scikit-Learn implementation of DBSCAN. To implement the method to choose ε you will need to understand it, as described in the paper. This is part of the assignment.

In addition, examine the performance of each algorithm (K-Means and DBSCAN) by varying the main parameter of each one (neighbourhood distance ε and number of clusters k; you can leave the other parameters with their default values) and using an internal index, the silhouette score, and external indexes computed from the labels available: the Rand index, Precision, Recall, the F1 measure and the adjusted Rand index. Note that the adjusted Rand index can be computed using the adjusted_rand_score function and the silhouette score using silhouette_score, both from sklearn.metrics.

Finally, select some parameter values for closer examination by visually inspecting the clusters generated. For this you can use the report_clusters(ids, labels, report_file) function in the tp2_aux.py module. Considering all the information gathered at this stage, recommend a procedure for the biologists that will help them process the segmented images, both for cell classification and to help discard segmentation errors. Optional exercise (for 2 points out of 20)

Implement the bissecting K-Means hierarchical clustering algorithm, as described in lecture 19. This can be done using the KMeans classifier available in the Scikit-Learn library to split each cluster into two sub-clusters with k = 2. Repeat this process by splitting the cluster with the largest number of examples in each iteration for a predetermined number of iterations. The output should be a list of lists, with each list corresponding to one example and listing all cluster labels to which the example was assigned, in order. Here is an example of using bissecting K-Means for three iterations on five examples. The first example was placed on cluster of index 1 in the first iteration, with the remainder on cluster of index 0. Then the third example was placed on sub-cluster of index 1, the other three on the sub-cluster of index 0. Of these, the second and fourth examples were placed in sub-sub-cluster 0 ([0, 0, 0]) and the fifth example on sub-sub-cluster of index 1 ([0, 0, 1]):

[ [1], [0, 0, 0], [0, 1], [0, 0, 0], [0, 0, 1] ]

If you want to examine the clusters generated after implementing the bissecting K-Means algorithm, you can use the function report_clusters_hierarchical(ixs,label_lists,report_file). This function works similarly to the report_clusters function but expects the labels to be a list of lists as described above.