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1 parent 18ed663 commit 1fc5cedd38d6eafa78644d45f2904b1bea205ed5 @saksham committed Mar 15, 2012
@@ -217,12 +217,13 @@ Morphology of mitochondria is interesting for cell biologists because it
image from the stack of images of the worms obtained from the microscope.
This process is biased because the biologists working with the worm have
prior expectation on the morphology.
- Moreover, the human based image analysis is time intensive.
+ Moreover, the human based image analysis is time consuming.
There is, hence, a need for an unbiased metric for the morphology of mitochondr
ia, and possible automation of the morphometric analysis.
In this report, the authors suggest use of two features for classification
- of mitochondria and present their findings on how different machine learning
- algorithms MLE, logistic regression, SVM and neural networks.
+ of mitochondria and present their findings on performance of the machine
+ learning algorithms MLE, logistic regression, SVM and neural networks on
+ the classification task.
\end_layout
\begin_layout Section
@@ -240,7 +241,7 @@ filename "background.lyx"
\end_layout
\begin_layout Section
-Data Acquisition, Processing and Feature Extraction (something like that)
+Data Pre-processing and Feature Extraction
\end_layout
\begin_layout Standard
@@ -250,7 +251,9 @@ name "sec:data-acquisition"
\end_inset
-
+This section describes how the images in the stack file are processed before
+ classification can be done.
+
\end_layout
\begin_layout Standard
@@ -276,9 +279,9 @@ name "sec:Classification"
\begin_layout Standard
Four machine learning algorithms: maximum likelihood estimation (MLE), logistic
- regression, support vector machine (SVM) and neural networks, were used
- for classifying the mitochondria from the sharpest image.
- Each of them is discussed in detail below.
+ regression, support vector machine (SVM) and neural networks (NN), were
+ used for classifying the mitochondria from the sharpest image.
+ Each of them is discussed in detail in this section.
\end_layout
\begin_layout Subsection
@@ -106,17 +106,13 @@ slice
These are attributes like “circularity” (shape compared to a perfect circle),
length of one mitochondria, kind of clustering, etc.
These data are used to classify the morphology of the mitochondria into
- three classes:
+ two classes:
\shape italic
fragmented
\shape default
-,
-\shape italic
-tubular
-\shape default
and
\shape italic
-in between
+tubular
\shape default
.
As this process involves human based classification, it is prone to biases
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